From bcb9a0f43486fd1a1e9608b695e181f30af582b0 Mon Sep 17 00:00:00 2001
From: protomotions <protomotions.nvidia@gmail.com>
Date: Thu, 15 Oct 2026 22:55:29 -0700
Subject: [PATCH 1/2] Improve simulator correctness and training reliability

Correct MuJoCo velocity frames and actuator torque handling, integrate task
keyboard controls into its viewer, and improve video recording.

Improve IsaacLab scene cloning and evaluation lifecycle handling, restore model
rollout context after evaluation, and flush stale contacts before restoration.
Sequential scene assignment currently guarantees the intended mapping for
single-object scenes; general multi-object scene assignment remains limited.

Normalize massless robot links before simulator import and repair Newton's
explicit proportional and torque control paths. Correct SOMA pelvis keypoints
used by robot retargeting.

Improve configuration validation, termination threshold forwarding, observation
noise propagation, cluster retry handling, and artifact copy-back reliability.
Add regression tests and clarify pretrained model licensing and notices.

Align newly built BeyondMimic controller configs with quarter-effort action
scaling and use a squared-L2 raw-action-rate penalty in the BM L2C2 recipe.
Existing saved controller configs retain their original scales and bindings.

Pretrained model weights and serialized model configurations are unchanged.

Signed-off-by: protomotions <protomotions.nvidia@gmail.com>
---
 .github/workflows/public-ci.yml               |  11 +
 CONTRIBUTING.md                               |  40 +-
 README.md                                     |   7 +-
 data/pretrained_models/AGENTS.md              |  11 +
 .../gpc_prior/soma_bones/LICENSE.pdf          | Bin 0 -> 79005 bytes
 .../gpc_prior/soma_bones/MODEL_CARD.md        |  12 +
 .../gpc_prior/soma_bones/NOTICE.txt           |   1 +
 .../masked_mimic/smpl/LICENSE.pdf             | Bin 0 -> 79005 bytes
 .../masked_mimic/smpl/MODEL_CARD.md           |  12 +
 .../masked_mimic/smpl/NOTICE.txt              |   1 +
 .../g1-bones-deploy/LICENSE.pdf               | Bin 0 -> 79005 bytes
 .../g1-bones-deploy/MODEL_CARD.md             |  12 +
 .../motion_tracker/g1-bones-deploy/NOTICE.txt |   1 +
 .../motion_tracker/smpl-terrains/LICENSE.pdf  | Bin 0 -> 79005 bytes
 .../smpl-terrains/MODEL_CARD.md               |  12 +
 .../motion_tracker/smpl-terrains/NOTICE.txt   |   1 +
 .../motion_tracker/smpl/LICENSE.pdf           | Bin 0 -> 79005 bytes
 .../motion_tracker/smpl/MODEL_CARD.md         |  12 +
 .../motion_tracker/smpl/NOTICE.txt            |   1 +
 .../motion_tracker/soma-bones/LICENSE.pdf     | Bin 0 -> 79005 bytes
 .../motion_tracker/soma-bones/MODEL_CARD.md   |  12 +
 .../motion_tracker/soma-bones/NOTICE.txt      |   1 +
 .../motion_tracker/soma_bones_fsq/LICENSE.pdf | Bin 0 -> 79005 bytes
 .../soma_bones_fsq/MODEL_CARD.md              |  12 +
 .../motion_tracker/soma_bones_fsq/NOTICE.txt  |   1 +
 .../soma_bones_fsq_amp_muon/LICENSE.pdf       | Bin 0 -> 79005 bytes
 .../soma_bones_fsq_amp_muon/MODEL_CARD.md     |  12 +
 .../soma_bones_fsq_amp_muon/NOTICE.txt        |   1 +
 data/scripts/keypoint_utils.py                |  16 +-
 docs/source/api_reference/scripts.rst         |  14 +-
 .../getting_started/pretrained_models.rst     |   7 +
 .../workflows/retargeting_pyroki.rst          |  14 +
 docs/source/user_guide/configuration.rst      |  22 +
 docs/source/user_guide/experiments.rst        |  13 +
 .../experiments/masked_mimic/transformer.py   |   3 +-
 examples/experiments/mimic/mlp_bm_l2c2.py     |   6 +-
 legal/MODEL-LICENSING.md                      |  50 ++
 legal/NVIDIA-Open-Model-License.pdf           | Bin 0 -> 79005 bytes
 legal/model-license-exceptions.json           |   1 +
 protomotions/agents/base_agent/agent.py       |  26 +-
 protomotions/agents/base_agent/model.py       |  26 +
 protomotions/agents/common/common.py          |  12 +
 protomotions/agents/common/config.py          |  57 ++
 protomotions/agents/evaluators/config.py      |  14 -
 .../agents/evaluators/mimic_evaluator.py      | 123 ++--
 protomotions/envs/action/action_functions.py  |   8 +-
 protomotions/envs/base_env/env.py             |   8 +
 protomotions/envs/base_env/utils.py           |   3 +-
 protomotions/envs/component_factories.py      |  15 +
 protomotions/envs/context_views.py            |  14 +
 protomotions/envs/mdp_component.py            |  57 +-
 protomotions/envs/rewards/__init__.py         |   2 +
 protomotions/envs/rewards/regularization.py   |   9 +
 protomotions/inference_agent.py               |   9 +
 protomotions/robot_configs/amp.py             |   9 +
 protomotions/robot_configs/smpl.py            |   9 +
 protomotions/robot_configs/smplx.py           |   9 +
 protomotions/robot_configs/soma23.py          |   9 +
 .../base_simulator/robot_mass_properties.py   | 127 ++++
 .../simulator/base_simulator/simulator.py     | 125 ++--
 protomotions/simulator/factory.py             |  21 +
 protomotions/simulator/genesis/simulator.py   |   7 +-
 protomotions/simulator/isaacgym/simulator.py  |   6 +-
 protomotions/simulator/isaaclab/simulator.py  |  79 +++
 .../simulator/isaaclab/utils/mjcf_to_usd.py   |  11 +-
 protomotions/simulator/mujoco/config.py       |  20 +-
 protomotions/simulator/mujoco/keyboard.py     | 198 ++----
 protomotions/simulator/mujoco/recording.py    |  81 +++
 protomotions/simulator/mujoco/simulator.py    | 206 ++++--
 protomotions/simulator/newton/simulator.py    |  41 +-
 protomotions/tests/test_action_functions.py   |  35 +-
 .../tests/test_base_agent_fit_helpers.py      |  45 ++
 protomotions/tests/test_base_env_helpers.py   |  50 ++
 .../tests/test_base_simulator_lifecycle.py    |  86 ++-
 protomotions/tests/test_cluster_retry_lib.py  | 611 ++++++++++++++++++
 .../tests/test_common_module_container.py     | 137 +++-
 ...t_context_views_and_component_factories.py |   7 +
 .../tests/test_example_experiment_configs.py  |  30 +
 .../tests/test_inference_agent_script.py      |   9 +
 protomotions/tests/test_lepton_launcher.py    |  17 +-
 protomotions/tests/test_lepton_train_loop.py  |  12 +-
 .../test_mdp_component_metadata_forwarding.py | 302 +++++++++
 .../tests/test_mimic_evaluator_helpers.py     | 195 +++++-
 protomotions/tests/test_model_licenses.py     | 115 ++++
 .../tests/test_mujoco_actuators_and_reset.py  | 266 ++++++++
 .../tests/test_mujoco_body_velocity.py        |  89 +++
 protomotions/tests/test_mujoco_keyboard.py    | 235 +++++--
 protomotions/tests/test_mujoco_recording.py   | 126 +++-
 .../tests/test_mujoco_root_ang_vel.py         | 111 ++++
 .../tests/test_newton_explicit_control.py     | 108 ++++
 protomotions/tests/test_newton_park_envs.py   |  44 --
 .../tests/test_noisy_current_state_view.py    | 128 ++++
 .../tests/test_pretrained_model_cards.py      |   1 +
 .../tests/test_prior_peft_agent_helpers.py    |   6 +
 .../tests/test_retargeting_keypoints.py       |  87 +++
 protomotions/tests/test_reward_kernels.py     |   8 +
 .../tests/test_robot_marker_inertias.py       | 127 ++++
 .../tests/test_simulator_factory_and_utils.py |  15 +
 .../tests/test_train_agent_helpers.py         |  28 +
 protomotions/tests/test_train_slurm_site.py   |  16 +-
 .../tests/test_train_slurm_startup_retry.py   | 332 ++--------
 .../tests/test_train_slurm_watchdog.py        |  10 +-
 protomotions/train_agent.py                   | 114 +++-
 protomotions/train_lepton.py                  |  36 +-
 protomotions/train_slurm.py                   | 326 +++-------
 protomotions/utils/retargeting_keypoints.py   |  65 ++
 pyroki/batch_retarget_to_g1_from_keypoints.py |  77 ++-
 .../batch_retarget_to_h1_2_from_keypoints.py  |  46 +-
 scripts/check_model_licenses.py               | 104 +++
 scripts/cluster/lepton_train_loop.sh          | 181 ++----
 scripts/cluster/retry_lib.sh                  | 306 +++++++++
 111 files changed, 5046 insertions(+), 1257 deletions(-)
 create mode 100644 data/pretrained_models/AGENTS.md
 create mode 100644 data/pretrained_models/gpc_prior/soma_bones/LICENSE.pdf
 create mode 100644 data/pretrained_models/gpc_prior/soma_bones/NOTICE.txt
 create mode 100644 data/pretrained_models/masked_mimic/smpl/LICENSE.pdf
 create mode 100644 data/pretrained_models/masked_mimic/smpl/NOTICE.txt
 create mode 100644 data/pretrained_models/motion_tracker/g1-bones-deploy/LICENSE.pdf
 create mode 100644 data/pretrained_models/motion_tracker/g1-bones-deploy/NOTICE.txt
 create mode 100644 data/pretrained_models/motion_tracker/smpl-terrains/LICENSE.pdf
 create mode 100644 data/pretrained_models/motion_tracker/smpl-terrains/NOTICE.txt
 create mode 100644 data/pretrained_models/motion_tracker/smpl/LICENSE.pdf
 create mode 100644 data/pretrained_models/motion_tracker/smpl/NOTICE.txt
 create mode 100644 data/pretrained_models/motion_tracker/soma-bones/LICENSE.pdf
 create mode 100644 data/pretrained_models/motion_tracker/soma-bones/NOTICE.txt
 create mode 100644 data/pretrained_models/motion_tracker/soma_bones_fsq/LICENSE.pdf
 create mode 100644 data/pretrained_models/motion_tracker/soma_bones_fsq/NOTICE.txt
 create mode 100644 data/pretrained_models/motion_tracker/soma_bones_fsq_amp_muon/LICENSE.pdf
 create mode 100644 data/pretrained_models/motion_tracker/soma_bones_fsq_amp_muon/NOTICE.txt
 create mode 100644 legal/MODEL-LICENSING.md
 create mode 100644 legal/NVIDIA-Open-Model-License.pdf
 create mode 100644 legal/model-license-exceptions.json
 create mode 100644 protomotions/simulator/base_simulator/robot_mass_properties.py
 create mode 100644 protomotions/simulator/mujoco/recording.py
 create mode 100644 protomotions/tests/test_cluster_retry_lib.py
 create mode 100644 protomotions/tests/test_mdp_component_metadata_forwarding.py
 create mode 100644 protomotions/tests/test_model_licenses.py
 create mode 100644 protomotions/tests/test_mujoco_actuators_and_reset.py
 create mode 100644 protomotions/tests/test_mujoco_body_velocity.py
 create mode 100644 protomotions/tests/test_mujoco_root_ang_vel.py
 create mode 100644 protomotions/tests/test_newton_explicit_control.py
 delete mode 100644 protomotions/tests/test_newton_park_envs.py
 create mode 100644 protomotions/tests/test_noisy_current_state_view.py
 create mode 100644 protomotions/tests/test_retargeting_keypoints.py
 create mode 100644 protomotions/tests/test_robot_marker_inertias.py
 create mode 100644 protomotions/utils/retargeting_keypoints.py
 create mode 100644 scripts/check_model_licenses.py
 create mode 100644 scripts/cluster/retry_lib.sh

diff --git a/.github/workflows/public-ci.yml b/.github/workflows/public-ci.yml
index 6218f80dd..938fb3af0 100644
--- a/.github/workflows/public-ci.yml
+++ b/.github/workflows/public-ci.yml
@@ -40,6 +40,17 @@ env:
   GITLEAKS_SHA256: "5bc41815076e6ed6ef8fbecc9d9b75bcae31f39029ceb55da08086315316e3ba"
 
 jobs:
+  model-licenses:
+    name: Model release licenses
+    if: github.repository == 'NVlabs/ProtoMotions'
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with: { lfs: false }
+      - uses: actions/setup-python@v5
+        with: { python-version: "3.10" }
+      - run: python scripts/check_model_licenses.py
+
   # Catches a credential in a contributed pull request before it is merged --
   # the one class of mistake that rewriting history afterwards does not undo,
   # because the commit stays reachable from the pull request ref forever.
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 891084c37..ba1ac1c05 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -2,6 +2,44 @@
 
 We started ProtoMotions3 to promote open research and help all members of the community. All participation in this project is open and not bound to corporate affiliation. 
 
+## Automated Checks
+
+Linting, formatting, license headers and spell checking all run through
+[pre-commit](https://pre-commit.com/). Install the hook once and every commit is checked
+before it is created:
+
+```bash
+pip install pre-commit
+pre-commit install
+```
+
+**Run these tools through pre-commit rather than invoking them yourself.**
+`.pre-commit-config.yaml` pins an exact version of each tool, and pre-commit installs
+those versions into an isolated environment of its own. A `ruff` that happens to be on
+your `PATH` is almost certainly a different version, and the formatter's output changes
+between releases — so a file your local `ruff` wants to reformat may be one the pinned
+version already considers correct. Going through pre-commit is what makes everyone's
+result the same, and the same as CI's.
+
+To check specific files without committing:
+
+```bash
+pre-commit run --files <file1> <file2> ...
+```
+
+Prefer that over `pre-commit run --all-files`: parts of the tree predate these checks, so
+running over everything produces a large diff unrelated to your change.
+
+## Releasing models
+
+Code remains Apache-2.0. NVIDIA-provided model releases default to the NVIDIA
+Open Model License unless explicitly approved otherwise. Follow the
+[model-release checklist](legal/MODEL-LICENSING.md): include a License section
+in the model card, the complete license and an attribution notice with every
+model package, including exported models and adapters. Document any exception
+in the card and the reviewed exception manifest. Run
+`python scripts/check_model_licenses.py` before submitting a model release.
+
 ## Code Reviews
 
 All submissions, including submissions by project members, require review. We use GitHub pull requests for this purpose. Consult
@@ -59,4 +97,4 @@ All submissions, including submissions by project members, require review. We us
         personal information I submit with it, including my sign-off) is
         maintained indefinitely and may be redistributed consistent with
         this project or the open source license(s) involved.
-  ```
\ No newline at end of file
+  ```
diff --git a/README.md b/README.md
index 3eb18647f..5118eef4f 100644
--- a/README.md
+++ b/README.md
@@ -234,7 +234,12 @@ We welcome contributions! Please read our [**Contributing Guide**](CONTRIBUTING.
 
 ## License
 
-ProtoMotions3 is released under the [**Apache-2.0 License**](LICENSE.md).
+ProtoMotions3 source code is released under the [**Apache-2.0 License**](LICENSE.md).
+
+NVIDIA-provided pretrained models are released under the **NVIDIA Open Model
+License**, unless their model card explicitly states otherwise. Each model
+directory includes its model card, license and attribution notice. See
+[model licensing and redistribution](legal/MODEL-LICENSING.md).
 
 Third-party software and bundled asset notices are listed in [legal/](legal/), including Unitree, BeyondMimic, Isaac Lab, and SMPL/SMPL-H attribution and license notices.
 
diff --git a/data/pretrained_models/AGENTS.md b/data/pretrained_models/AGENTS.md
new file mode 100644
index 000000000..fb40aef11
--- /dev/null
+++ b/data/pretrained_models/AGENTS.md
@@ -0,0 +1,11 @@
+# Model release requirements
+
+- Follow [the model licensing policy](../../legal/MODEL-LICENSING.md).
+- NVIDIA-provided model releases default to the NVIDIA Open Model License;
+  source code remains Apache-2.0.
+- Every model needs a License section in its model card, a complete license
+  copy and an attribution notice. Include these in standalone exports too.
+- Alternative licenses require a reviewed exception recorded in both the card
+  and the exception manifest. Preserve applicable third-party terms.
+- Run `python scripts/check_model_licenses.py` from the repository root.
+- Do not modify weights or serialized configs just to change licensing notices.
diff --git a/data/pretrained_models/gpc_prior/soma_bones/LICENSE.pdf b/data/pretrained_models/gpc_prior/soma_bones/LICENSE.pdf
new file mode 100644
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diff --git a/data/pretrained_models/gpc_prior/soma_bones/MODEL_CARD.md b/data/pretrained_models/gpc_prior/soma_bones/MODEL_CARD.md
index 8078a2c1b..a4df71cd8 100644
--- a/data/pretrained_models/gpc_prior/soma_bones/MODEL_CARD.md
+++ b/data/pretrained_models/gpc_prior/soma_bones/MODEL_CARD.md
@@ -1,5 +1,17 @@
 # SOMA BONES-SEED GPC Prior
 
+## License
+
+Model license: NVIDIA Open Model License
+
+The model weights, checkpoints, serialized model configurations and exported
+models are licensed under the [NVIDIA Open Model License](LICENSE.pdf).
+See [NOTICE.txt](NOTICE.txt) for attribution. Include both files when
+redistributing these artifacts.
+
+ProtoMotions source code remains Apache-2.0. Training datasets and robot assets
+have separate terms; this model license does not grant rights to those materials.
+
 ## Overview
 
 This checkpoint is an autoregressive Generative Pre-trained Controller (GPC)
diff --git a/data/pretrained_models/gpc_prior/soma_bones/NOTICE.txt b/data/pretrained_models/gpc_prior/soma_bones/NOTICE.txt
new file mode 100644
index 000000000..559a7c65e
--- /dev/null
+++ b/data/pretrained_models/gpc_prior/soma_bones/NOTICE.txt
@@ -0,0 +1 @@
+Licensed by NVIDIA Corporation under the NVIDIA Open Model License
diff --git a/data/pretrained_models/masked_mimic/smpl/LICENSE.pdf b/data/pretrained_models/masked_mimic/smpl/LICENSE.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..d803837f7b3281922645340b408fc4b4c3d23cdb
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diff --git a/data/pretrained_models/masked_mimic/smpl/MODEL_CARD.md b/data/pretrained_models/masked_mimic/smpl/MODEL_CARD.md
index 35b073e7c..6947182c6 100644
--- a/data/pretrained_models/masked_mimic/smpl/MODEL_CARD.md
+++ b/data/pretrained_models/masked_mimic/smpl/MODEL_CARD.md
@@ -1,5 +1,17 @@
 # SMPL MaskedMimic
 
+## License
+
+Model license: NVIDIA Open Model License
+
+The model weights, checkpoints, serialized model configurations and exported
+models are licensed under the [NVIDIA Open Model License](LICENSE.pdf).
+See [NOTICE.txt](NOTICE.txt) for attribution. Include both files when
+redistributing these artifacts.
+
+ProtoMotions source code remains Apache-2.0. Training datasets and robot assets
+have separate terms; this model license does not grant rights to those materials.
+
 ## Overview
 
 This is a MaskedMimic controller for the SMPL humanoid. The policy produces
diff --git a/data/pretrained_models/masked_mimic/smpl/NOTICE.txt b/data/pretrained_models/masked_mimic/smpl/NOTICE.txt
new file mode 100644
index 000000000..559a7c65e
--- /dev/null
+++ b/data/pretrained_models/masked_mimic/smpl/NOTICE.txt
@@ -0,0 +1 @@
+Licensed by NVIDIA Corporation under the NVIDIA Open Model License
diff --git a/data/pretrained_models/motion_tracker/g1-bones-deploy/LICENSE.pdf b/data/pretrained_models/motion_tracker/g1-bones-deploy/LICENSE.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..d803837f7b3281922645340b408fc4b4c3d23cdb
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diff --git a/data/pretrained_models/motion_tracker/g1-bones-deploy/MODEL_CARD.md b/data/pretrained_models/motion_tracker/g1-bones-deploy/MODEL_CARD.md
index 0ea4c5610..fc03f2e03 100644
--- a/data/pretrained_models/motion_tracker/g1-bones-deploy/MODEL_CARD.md
+++ b/data/pretrained_models/motion_tracker/g1-bones-deploy/MODEL_CARD.md
@@ -1,5 +1,17 @@
 # G1 BONES-SEED Deployment Tracker
 
+## License
+
+Model license: NVIDIA Open Model License
+
+The model weights, checkpoints, serialized model configurations and exported
+models are licensed under the [NVIDIA Open Model License](LICENSE.pdf).
+See [NOTICE.txt](NOTICE.txt) for attribution. Include both files when
+redistributing these artifacts.
+
+ProtoMotions source code remains Apache-2.0. Training datasets and robot assets
+have separate terms; this model license does not grant rights to those materials.
+
 ## Overview
 
 This is a general motion-tracking policy for the Unitree G1 humanoid. It is the
diff --git a/data/pretrained_models/motion_tracker/g1-bones-deploy/NOTICE.txt b/data/pretrained_models/motion_tracker/g1-bones-deploy/NOTICE.txt
new file mode 100644
index 000000000..559a7c65e
--- /dev/null
+++ b/data/pretrained_models/motion_tracker/g1-bones-deploy/NOTICE.txt
@@ -0,0 +1 @@
+Licensed by NVIDIA Corporation under the NVIDIA Open Model License
diff --git a/data/pretrained_models/motion_tracker/smpl-terrains/LICENSE.pdf b/data/pretrained_models/motion_tracker/smpl-terrains/LICENSE.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..d803837f7b3281922645340b408fc4b4c3d23cdb
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diff --git a/data/pretrained_models/motion_tracker/smpl-terrains/MODEL_CARD.md b/data/pretrained_models/motion_tracker/smpl-terrains/MODEL_CARD.md
index 0ba15f010..80ad6c5c3 100644
--- a/data/pretrained_models/motion_tracker/smpl-terrains/MODEL_CARD.md
+++ b/data/pretrained_models/motion_tracker/smpl-terrains/MODEL_CARD.md
@@ -1,5 +1,17 @@
 # SMPL AMASS Terrain Tracker
 
+## License
+
+Model license: NVIDIA Open Model License
+
+The model weights, checkpoints, serialized model configurations and exported
+models are licensed under the [NVIDIA Open Model License](LICENSE.pdf).
+See [NOTICE.txt](NOTICE.txt) for attribution. Include both files when
+redistributing these artifacts.
+
+ProtoMotions source code remains Apache-2.0. Training datasets and robot assets
+have separate terms; this model license does not grant rights to those materials.
+
 ## Overview
 
 This is a general SMPL motion tracker trained on a mixture of procedural terrain
diff --git a/data/pretrained_models/motion_tracker/smpl-terrains/NOTICE.txt b/data/pretrained_models/motion_tracker/smpl-terrains/NOTICE.txt
new file mode 100644
index 000000000..559a7c65e
--- /dev/null
+++ b/data/pretrained_models/motion_tracker/smpl-terrains/NOTICE.txt
@@ -0,0 +1 @@
+Licensed by NVIDIA Corporation under the NVIDIA Open Model License
diff --git a/data/pretrained_models/motion_tracker/smpl/LICENSE.pdf b/data/pretrained_models/motion_tracker/smpl/LICENSE.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..d803837f7b3281922645340b408fc4b4c3d23cdb
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diff --git a/data/pretrained_models/motion_tracker/smpl/MODEL_CARD.md b/data/pretrained_models/motion_tracker/smpl/MODEL_CARD.md
index 7589e0812..61ed6719f 100644
--- a/data/pretrained_models/motion_tracker/smpl/MODEL_CARD.md
+++ b/data/pretrained_models/motion_tracker/smpl/MODEL_CARD.md
@@ -1,5 +1,17 @@
 # SMPL AMASS Flat-Terrain Tracker
 
+## License
+
+Model license: NVIDIA Open Model License
+
+The model weights, checkpoints, serialized model configurations and exported
+models are licensed under the [NVIDIA Open Model License](LICENSE.pdf).
+See [NOTICE.txt](NOTICE.txt) for attribution. Include both files when
+redistributing these artifacts.
+
+ProtoMotions source code remains Apache-2.0. Training datasets and robot assets
+have separate terms; this model license does not grant rights to those materials.
+
 ## Overview
 
 This is a general motion-tracking policy for the SMPL humanoid on flat terrain.
diff --git a/data/pretrained_models/motion_tracker/smpl/NOTICE.txt b/data/pretrained_models/motion_tracker/smpl/NOTICE.txt
new file mode 100644
index 000000000..559a7c65e
--- /dev/null
+++ b/data/pretrained_models/motion_tracker/smpl/NOTICE.txt
@@ -0,0 +1 @@
+Licensed by NVIDIA Corporation under the NVIDIA Open Model License
diff --git a/data/pretrained_models/motion_tracker/soma-bones/LICENSE.pdf b/data/pretrained_models/motion_tracker/soma-bones/LICENSE.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..d803837f7b3281922645340b408fc4b4c3d23cdb
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diff --git a/data/pretrained_models/motion_tracker/soma-bones/MODEL_CARD.md b/data/pretrained_models/motion_tracker/soma-bones/MODEL_CARD.md
index dbf6de433..5eb3f8d0b 100644
--- a/data/pretrained_models/motion_tracker/soma-bones/MODEL_CARD.md
+++ b/data/pretrained_models/motion_tracker/soma-bones/MODEL_CARD.md
@@ -1,5 +1,17 @@
 # SOMA BONES-SEED Motion Tracker
 
+## License
+
+Model license: NVIDIA Open Model License
+
+The model weights, checkpoints, serialized model configurations and exported
+models are licensed under the [NVIDIA Open Model License](LICENSE.pdf).
+See [NOTICE.txt](NOTICE.txt) for attribution. Include both files when
+redistributing these artifacts.
+
+ProtoMotions source code remains Apache-2.0. Training datasets and robot assets
+have separate terms; this model license does not grant rights to those materials.
+
 ## Overview
 
 This is a continuous-action general motion tracker for the SOMA 23-body
diff --git a/data/pretrained_models/motion_tracker/soma-bones/NOTICE.txt b/data/pretrained_models/motion_tracker/soma-bones/NOTICE.txt
new file mode 100644
index 000000000..559a7c65e
--- /dev/null
+++ b/data/pretrained_models/motion_tracker/soma-bones/NOTICE.txt
@@ -0,0 +1 @@
+Licensed by NVIDIA Corporation under the NVIDIA Open Model License
diff --git a/data/pretrained_models/motion_tracker/soma_bones_fsq/LICENSE.pdf b/data/pretrained_models/motion_tracker/soma_bones_fsq/LICENSE.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..d803837f7b3281922645340b408fc4b4c3d23cdb
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diff --git a/data/pretrained_models/motion_tracker/soma_bones_fsq/MODEL_CARD.md b/data/pretrained_models/motion_tracker/soma_bones_fsq/MODEL_CARD.md
index 8454c7153..926adae72 100644
--- a/data/pretrained_models/motion_tracker/soma_bones_fsq/MODEL_CARD.md
+++ b/data/pretrained_models/motion_tracker/soma_bones_fsq/MODEL_CARD.md
@@ -1,5 +1,17 @@
 # SOMA BONES-SEED FSQ Motion Tracker
 
+## License
+
+Model license: NVIDIA Open Model License
+
+The model weights, checkpoints, serialized model configurations and exported
+models are licensed under the [NVIDIA Open Model License](LICENSE.pdf).
+See [NOTICE.txt](NOTICE.txt) for attribution. Include both files when
+redistributing these artifacts.
+
+ProtoMotions source code remains Apache-2.0. Training datasets and robot assets
+have separate terms; this model license does not grant rights to those materials.
+
 ## Overview
 
 This is a SOMA motion tracker with a finite-scalar-quantization (FSQ) bottleneck.
diff --git a/data/pretrained_models/motion_tracker/soma_bones_fsq/NOTICE.txt b/data/pretrained_models/motion_tracker/soma_bones_fsq/NOTICE.txt
new file mode 100644
index 000000000..559a7c65e
--- /dev/null
+++ b/data/pretrained_models/motion_tracker/soma_bones_fsq/NOTICE.txt
@@ -0,0 +1 @@
+Licensed by NVIDIA Corporation under the NVIDIA Open Model License
diff --git a/data/pretrained_models/motion_tracker/soma_bones_fsq_amp_muon/LICENSE.pdf b/data/pretrained_models/motion_tracker/soma_bones_fsq_amp_muon/LICENSE.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..d803837f7b3281922645340b408fc4b4c3d23cdb
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diff --git a/data/pretrained_models/motion_tracker/soma_bones_fsq_amp_muon/MODEL_CARD.md b/data/pretrained_models/motion_tracker/soma_bones_fsq_amp_muon/MODEL_CARD.md
index 0beae39fc..a04800cee 100644
--- a/data/pretrained_models/motion_tracker/soma_bones_fsq_amp_muon/MODEL_CARD.md
+++ b/data/pretrained_models/motion_tracker/soma_bones_fsq_amp_muon/MODEL_CARD.md
@@ -1,5 +1,17 @@
 # SOMA BONES-SEED FSQ AMP Tracker
 
+## License
+
+Model license: NVIDIA Open Model License
+
+The model weights, checkpoints, serialized model configurations and exported
+models are licensed under the [NVIDIA Open Model License](LICENSE.pdf).
+See [NOTICE.txt](NOTICE.txt) for attribution. Include both files when
+redistributing these artifacts.
+
+ProtoMotions source code remains Apache-2.0. Training datasets and robot assets
+have separate terms; this model license does not grant rights to those materials.
+
 ## Overview
 
 This is a SOMA motion tracker that combines an FSQ actor bottleneck with
diff --git a/data/pretrained_models/motion_tracker/soma_bones_fsq_amp_muon/NOTICE.txt b/data/pretrained_models/motion_tracker/soma_bones_fsq_amp_muon/NOTICE.txt
new file mode 100644
index 000000000..559a7c65e
--- /dev/null
+++ b/data/pretrained_models/motion_tracker/soma_bones_fsq_amp_muon/NOTICE.txt
@@ -0,0 +1 @@
+Licensed by NVIDIA Corporation under the NVIDIA Open Model License
diff --git a/data/scripts/keypoint_utils.py b/data/scripts/keypoint_utils.py
index 5347bd4a4..5f693b561 100644
--- a/data/scripts/keypoint_utils.py
+++ b/data/scripts/keypoint_utils.py
@@ -2,6 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import torch
+from protomotions.utils.retargeting_keypoints import SOMA_PELVIS_KEYPOINT_SHIFT
 from protomotions.utils.rotations import quaternion_to_matrix
 from typing import List, Dict, Tuple
 
@@ -580,11 +581,13 @@ def extract_keypoints_from_motion_soma_skel(
         keypoint_orientations_quat, w_last=True
     )
 
-    # Pelvis offset: shift backward in local +y to create forward bias on all
-    # Body keypoints relative to root.
+    # Pelvis offset: store the pelvis keypoint 7 cm behind the Hips joint, as
+    # every SOMA keypoint file so far has it. The retargeters move it back onto
+    # the joint when they load the file (soma_pelvis_keypoint_on_hips in
+    # protomotions/utils/retargeting_keypoints.py).
     root_idx = conceptual_keypoint_names.index("pelvis")
     root_offset = torch.tensor(
-        [0.0, 0.07, 0.0], device=device, dtype=keypoint_positions.dtype
+        SOMA_PELVIS_KEYPOINT_SHIFT, device=device, dtype=keypoint_positions.dtype
     )
     R_root = keypoint_orientations_mat[:, root_idx, :, :]
     keypoint_positions[:, root_idx, :] += torch.einsum(
@@ -671,9 +674,10 @@ def extract_keypoints_from_motion_soma_skel(
                 dim=1,
             )
 
-        # 0.225 * 0.8 = 0.18m, matching retargeter torso_aux x.
-        # A 0.30m offset would produce 0.24m after scaling, creating
-        # a 6cm x-mismatch with the torso_aux (0.18) that pushes pelvis backward.
+        # 0.225 m in front of the shifted pelvis keypoint, i.e. 0.155 m in front
+        # of the Hips joint (0.124 m after G1's 0.8 scaling, against G1's 0.18 m
+        # torso_aux). It was sized as 0.225 * 0.8 = 0.18 m for a G1 pelvis matched
+        # to the shifted keypoint, which the retargeters no longer do.
         pelvis_idx = conceptual_keypoint_names.index("pelvis")
         p_local_pelvis = torch.tensor(
             [0.0, -0.225, 0.0], device=device, dtype=keypoint_positions.dtype
diff --git a/docs/source/api_reference/scripts.rst b/docs/source/api_reference/scripts.rst
index 97260ce1e..0c02fa49d 100644
--- a/docs/source/api_reference/scripts.rst
+++ b/docs/source/api_reference/scripts.rst
@@ -74,6 +74,19 @@ For kinematic motion playback without physics training state:
 * **R** - Reset environments
 * **Q** - Quit
 
+For MuJoCo, click the simulation viewer itself to give it keyboard focus;
+there is no separate controls window. Keys produce discrete presses (held-key
+state is not available from the passive viewer). Application callbacks run on
+the simulation thread before physics, not on the viewer thread.
+
+MuJoCo also assigns visualization shortcuts to task keys such as W, R and L.
+Task mode restores the startup visualization flags and geometry groups at each
+render synchronization. Press **.** to switch to native viewer shortcuts for
+debugging; task commands are disabled in that mode. Press **.** again to resume
+task controls and restore the startup visualization settings. Mouse orbit and
+zoom remain available in both modes. Native shortcuts execute before the Python
+callback, so a transient visual change may appear before the next synchronization.
+
 train_slurm.py
 --------------
 
@@ -106,4 +119,3 @@ See Also
 
 * :doc:`../getting_started/quickstart` - Getting started guide
 * :doc:`../user_guide/configuration` - Configuration system
-
diff --git a/docs/source/getting_started/pretrained_models.rst b/docs/source/getting_started/pretrained_models.rst
index 633600823..a880a155b 100644
--- a/docs/source/getting_started/pretrained_models.rst
+++ b/docs/source/getting_started/pretrained_models.rst
@@ -4,6 +4,13 @@ Pre-trained Models
 Each shipped checkpoint has a model card beside it with the training recipe,
 input and output contract, artifact roles, intended use, and limitations.
 
+NVIDIA-provided pretrained models use the **NVIDIA Open Model License** unless
+their card explicitly specifies otherwise; ProtoMotions source code remains
+Apache-2.0. Each model directory includes a full license and attribution notice.
+Keep these files with redistributed checkpoints and exported models. See the
+`model licensing policy <https://github.com/NVlabs/ProtoMotions/blob/main/legal/MODEL-LICENSING.md>`__
+for details. Training datasets and robot assets retain their separate terms.
+
 Unless its model card says otherwise, a policy should be used on its training
 simulator. The recommended checkpoints were trained or fine-tuned in IsaacLab.
 The G1 deployment tracker is the only current policy trained with the full
diff --git a/docs/source/tutorials/workflows/retargeting_pyroki.rst b/docs/source/tutorials/workflows/retargeting_pyroki.rst
index b184dcf46..c996a4128 100644
--- a/docs/source/tutorials/workflows/retargeting_pyroki.rst
+++ b/docs/source/tutorials/workflows/retargeting_pyroki.rst
@@ -412,6 +412,20 @@ Comparing ``batch_retarget_to_g1_from_keypoints.py`` and
               * onp.array([1.1, 1.1, 1.0])[None, None, :]
           )
 
+   Take the vertical factor for the pelvis and legs from the robot/source
+   pelvis-to-ankle drop, not from the hip-joint height. Knee bend is very
+   sensitive to it: near a straight leg, a pelvis target only 1 cm too low
+   already bends G1's knees by about 20 degrees. Keep the x and y factors
+   equal, so the scaling does not depend on which way the motion is heading.
+
+   The robot matches the pelvis keypoint with its pelvis origin. Keypoint
+   extraction stores the SOMA pelvis keypoint 7 cm behind the Hips joint, so
+   ``load_motion_data`` moves it back onto the joint with
+   ``soma_pelvis_keypoint_on_hips`` from
+   ``protomotions/utils/retargeting_keypoints.py``. Call it in a new robot's
+   script too; otherwise every SOMA target lands about 7 cm too far forward
+   relative to the pelvis.
+
 3. **Auxiliary Point Offsets**
    
    Update hand and torso auxiliary point offsets in ``pc_alignment_cost()``:
diff --git a/docs/source/user_guide/configuration.rst b/docs/source/user_guide/configuration.rst
index b12a3f56c..038d07ca3 100644
--- a/docs/source/user_guide/configuration.rst
+++ b/docs/source/user_guide/configuration.rst
@@ -124,6 +124,28 @@ Robot configs in ``protomotions/robot_configs/`` define the robot:
 Using Configurations
 --------------------
 
+Marker Body Mass and Inertia
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Before importing a robot, the shared loading code resolves its MJCF masses.
+A zero-mass body (typically a fixed tracking marker) receives **0.001 kg (1 g)**
+and diagonal inertia of **4e-8 kg m²** at its local origin. These inertias
+correspond to a 1 g solid sphere with a 1 cm radius; the visual marker geometry
+does not need to change. This convention avoids the extremely small inertia
+of a visualization-sized sphere.
+
+Source MJCF files are unchanged. The loader prepares a temporary asset with
+explicit marker inertias, which IsaacGym, IsaacLab, Newton, Genesis and MuJoCo
+all consume. IsaacLab performs this step before MJCF-to-USD conversion and
+invalidates older conversion caches. PhysX articulation links cannot have zero
+mass; leaving a marker massless can silently give it the simulator's default
+mass instead.
+
+Ordinary physical bodies retain their authored or geometry-derived mass.
+Disabling mass randomization does not make a body massless: it uses the MJCF
+values after normalization. When enabled, body-mass randomization overrides
+the selected bodies and scales their inertia from the positive source properties.
+
 Basic Usage
 ~~~~~~~~~~~
 
diff --git a/docs/source/user_guide/experiments.rst b/docs/source/user_guide/experiments.rst
index 057e43308..781f502fe 100644
--- a/docs/source/user_guide/experiments.rst
+++ b/docs/source/user_guide/experiments.rst
@@ -27,6 +27,19 @@ reproduce the motion while maintaining physical plausibility.
 * ``mlp_bm_l2c2.py`` - BeyondMimic policy with a torso anchor and L2C2 smoothness regularization, aimed at sim2real
 * ``fsq.py`` - FSQ tracker over max-coordinates proprioception and target poses
 
+The BM recipe uses ``q_target = q_default + 0.25 * effort_limit / stiffness * action``
+without clipping the raw action or changing the robot's PD gains. Its action-rate
+reward is ``-0.1 * sum((action - previous_action)**2)`` on raw policy outputs,
+with no mean, square root, or timestep division. This is distinct from the L2C2
+model loss and from the processed-action L2 norm used by other recipes.
+
+The scale change applies to all newly built configs using
+``make_bm_pd_action_config``. The raw action-rate reward is selected explicitly
+by the BM L2C2 recipe; other recipes keep their existing reward bindings.
+Resume and inference use the action
+scales and reward bindings saved in ``resolved_configs.pt``; existing pretrained
+configs are not rewritten. Use a new experiment name to train with this recipe.
+
 **Example command**:
 
 .. code-block:: bash
diff --git a/examples/experiments/masked_mimic/transformer.py b/examples/experiments/masked_mimic/transformer.py
index d645e251b..e6542e602 100644
--- a/examples/experiments/masked_mimic/transformer.py
+++ b/examples/experiments/masked_mimic/transformer.py
@@ -205,7 +205,8 @@ def env_config(robot_cfg: RobotConfig, args: argparse.Namespace) -> EnvConfig:
 
     # Termination components configuration
     termination_components = {
-        "tracking_error": tracking_error_term_factory(threshold=0.25),
+        # Always ran at 0.5 m: the 0.25 m set here before never took effect.
+        "tracking_error": tracking_error_term_factory(threshold=0.5),
     }
 
     # Reward components
diff --git a/examples/experiments/mimic/mlp_bm_l2c2.py b/examples/experiments/mimic/mlp_bm_l2c2.py
index 47c6e1c37..6de5c857c 100644
--- a/examples/experiments/mimic/mlp_bm_l2c2.py
+++ b/examples/experiments/mimic/mlp_bm_l2c2.py
@@ -74,7 +74,7 @@ def env_config(robot_cfg: RobotConfig, args: argparse.Namespace) -> EnvConfig:
         max_coords_obs_factory,
         mimic_target_poses_max_coords_factory,
         previous_actions_factory,
-        action_smoothness_factory,
+        action_rate_l2_factory,
         global_anchor_ori_rew_factory,
         relative_body_pos_rew_factory,
         relative_body_ori_rew_factory,
@@ -131,7 +131,7 @@ def env_config(robot_cfg: RobotConfig, args: argparse.Namespace) -> EnvConfig:
             with_velocities=True,
             with_relative=True,
         ),
-        # Common observations (processed actions after tanh/clamp)
+        # Common observations (joint position targets after PD action scaling)
         "historical_previous_processed_actions": previous_actions_factory(
             history_steps=1, processed=True
         ),
@@ -164,7 +164,7 @@ def env_config(robot_cfg: RobotConfig, args: argparse.Namespace) -> EnvConfig:
             weight=1.0,
             sigma=3.14,
         ),
-        "action_rate": action_smoothness_factory(weight=-0.1),
+        "action_rate": action_rate_l2_factory(weight=-0.1),
         "limits_dof_pos": MdpComponent(
             compute_func=compute_soft_pos_limit_rew,
             dynamic_vars={
diff --git a/legal/MODEL-LICENSING.md b/legal/MODEL-LICENSING.md
new file mode 100644
index 000000000..53b5b564b
--- /dev/null
+++ b/legal/MODEL-LICENSING.md
@@ -0,0 +1,50 @@
+# Model release licensing
+
+ProtoMotions source code remains under [Apache-2.0](../LICENSE.md).
+NVIDIA-provided pretrained model weights, checkpoints, serialized model
+configurations and exported models are released under the **NVIDIA Open Model
+License**, unless a model card explicitly specifies an approved exception.
+This is a release policy for models distributed by this project, not a license
+imposed on every model that someone trains using the Apache-licensed code.
+
+The bundled [agreement](NVIDIA-Open-Model-License.pdf) is the unmodified
+[official NVIDIA PDF](https://www.nvidia.com/content/dam/en-zz/Solutions/license-agreements/enterprise-software/nvidia-open-model-license-agreements-24-10-2025.pdf),
+version October 24, 2025. The [official license page](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/)
+provides an HTML version. Source files, including model architectures and
+experiment scripts, keep their Apache SPDX headers. Third-party datasets,
+robot assets and other separately licensed components retain their own terms.
+This policy does not revoke rights previously granted under another license.
+
+## Publishing a model
+
+Keep each model under `data/pretrained_models/<family>/<model>/` and include:
+
+1. A `MODEL_CARD.md` with a `## License` section containing
+   `Model license: NVIDIA Open Model License`.
+2. A byte-for-byte copy of `legal/NVIDIA-Open-Model-License.pdf` named
+   `LICENSE.pdf` beside that card.
+3. A `NOTICE.txt` containing the attribution:
+   `Licensed by NVIDIA Corporation under the NVIDIA Open Model License`.
+4. Links to `LICENSE.pdf` and `NOTICE.txt` from the card's License section.
+   Keep the card's training-data provenance and applicable third-party notices.
+
+Include the license, notice and model card in standalone downloads and
+exported-model packages too; a weights-only or ONNX-only upload is not a complete
+release. Preserve applicable upstream terms when distributing fine-tuned models,
+adapters or derivatives; do not infer dataset redistribution rights from the
+model license.
+
+Run `python scripts/check_model_licenses.py` before publishing. CI runs this
+without loading checkpoints or downloading LFS payloads, and the release
+projection checks the actual outgoing tree.
+
+## Explicit exceptions
+
+Never silently relabel third-party weights. A maintainer-reviewed exception
+must specify the alternative license and its rationale in both the card and
+`legal/model-license-exceptions.json`. The JSON maps the model directory
+(relative to the repository) to `license`, `license_file`, and `reason`.
+The card uses `Model license: <license>` and
+`License exception: <reason>` in its License section. Bundle the full alternative
+license under the specified filename and a nonempty `NOTICE.txt`, linking both
+from that section. No current public model has an exception.
diff --git a/legal/NVIDIA-Open-Model-License.pdf b/legal/NVIDIA-Open-Model-License.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..d803837f7b3281922645340b408fc4b4c3d23cdb
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literal 0
HcmV?d00001

diff --git a/legal/model-license-exceptions.json b/legal/model-license-exceptions.json
new file mode 100644
index 000000000..0967ef424
--- /dev/null
+++ b/legal/model-license-exceptions.json
@@ -0,0 +1 @@
+{}
diff --git a/protomotions/agents/base_agent/agent.py b/protomotions/agents/base_agent/agent.py
index 6a385f347..f067dd442 100644
--- a/protomotions/agents/base_agent/agent.py
+++ b/protomotions/agents/base_agent/agent.py
@@ -702,15 +702,23 @@ def fit(self):
             self.fit_start_time = time.time()
         self.fabric.call("on_fit_start", self)
 
-        # Marks the transition from setup (env/scene build, which is what fails or
-        # wedges before training) into the training loop. train_slurm.py's opt-in
-        # startup watchdog disarms on the first "Epoch <n>" line: printing one here,
-        # at loop entry, means it disarms right after the scene build rather than
-        # only at the end of the first (potentially long, silent) data-collection
-        # epoch -- the rich progress line below flushes to a non-TTY log just once,
-        # at 100%, which would leave the watchdog armed through a healthy slow
-        # rollout. flush=True so it reaches the log before that silent window.
-        print(f"Epoch {self.current_epoch}: entering training loop", flush=True)
+        # Guarantee last.ckpt exists once training is reached: a resume point for a
+        # long first epoch, and the signal the cluster retry harness reads
+        # (scripts/cluster/retry_lib.sh) to tell "reached training" from a flaky
+        # scene build. Write it ONLY when absent. On a resume the loaded checkpoint
+        # already is the resume point, and re-writing here every attempt would
+        # advance last.ckpt's mtime with no real progress -- which the retry loop
+        # would misread as "training advanced", resetting its no-progress and
+        # exception-repeat counters and so masking a deterministic first-epoch crash
+        # instead of fast-failing it. Broadcast rank 0's view so every rank makes the
+        # SAME call: save() runs distributed collectives, and a split decision would
+        # deadlock. Safe to save here -- the forward pass during buffer registration
+        # above already materialized every LazyLinear parameter.
+        need_initial_ckpt = self.fabric.broadcast(
+            not (self.root_dir / "last.ckpt").exists()
+        )
+        if need_initial_ckpt:
+            self.save(checkpoint_name="last.ckpt")
 
         while self.current_epoch < self.max_epochs:
             self.epoch_start_time = time.time()
diff --git a/protomotions/agents/base_agent/model.py b/protomotions/agents/base_agent/model.py
index f9567270b..f55e6bb54 100644
--- a/protomotions/agents/base_agent/model.py
+++ b/protomotions/agents/base_agent/model.py
@@ -167,6 +167,32 @@ def reset_rollout_state(self, env_ids=None) -> None:
         for child in self._proto_children():
             child.reset_rollout_state(env_ids=env_ids)
 
+    def save_rollout_context(self) -> dict[str, torch.Tensor]:
+        """Copy every rollout-context buffer in this module tree.
+
+        A caller that runs its own episodes on the training envs, such as
+        evaluation, saves this first and passes it to ``load_rollout_context``
+        afterwards, so training resumes with the context it had.
+        """
+        return {
+            self._rollout_context_name(name, key): getattr(module, key).clone()
+            for name, module in self.named_modules()
+            if isinstance(module, ProtoMotionsTensorDictModule)
+            for key in module.rollout_state_specs()
+        }
+
+    def load_rollout_context(self, state: dict[str, torch.Tensor]) -> None:
+        """Write back rollout context returned by ``save_rollout_context``."""
+        for name, module in self.named_modules():
+            if not isinstance(module, ProtoMotionsTensorDictModule):
+                continue
+            for key in module.rollout_state_specs():
+                getattr(module, key).copy_(state[self._rollout_context_name(name, key)])
+
+    @staticmethod
+    def _rollout_context_name(module_name: str, key: str) -> str:
+        return f"{module_name}.{key}" if module_name else key
+
     def _reset_own_rollout_state(self, env_ids=None) -> None:
         """Reseed selected rows of this module's initialized rollout context."""
         for key, spec in self.rollout_state_specs().items():
diff --git a/protomotions/agents/common/common.py b/protomotions/agents/common/common.py
index 3c16af5b6..c5f2dccc9 100644
--- a/protomotions/agents/common/common.py
+++ b/protomotions/agents/common/common.py
@@ -249,6 +249,18 @@ def __init__(self, config: ModuleContainerConfig):
 
         self.models = nn.ModuleList()
 
+        # Cheap structural check first, so an ordering mistake is reported before
+        # any module is constructed. Building modules materializes lazy layers and,
+        # on a multi-rank job, an exception here kills one rank and leaves the rest
+        # blocked at the next collective -- which presents as a hang rather than as
+        # the error it is. See ModuleContainerConfig.find_key_flow_errors.
+        ordering_errors = self.config.find_key_flow_errors()
+        if ordering_errors:
+            raise AssertionError(
+                "ModuleContainer: module list is out of order.\n  "
+                + "\n  ".join(ordering_errors)
+            )
+
         # Build models and validate data flow
         # Container in_keys are "available" at the start (like sources)
         available_keys = set(self.config.in_keys)
diff --git a/protomotions/agents/common/config.py b/protomotions/agents/common/config.py
index 5209149ae..ef9ca2962 100644
--- a/protomotions/agents/common/config.py
+++ b/protomotions/agents/common/config.py
@@ -207,6 +207,12 @@ class ModuleContainerConfig:
 
     Modules are processed in order, with each module's outputs available to subsequent modules.
     Input keys are passed through, and all specified output keys must be produced by internal modules.
+
+    **``models`` is execution order, not a dependency graph.** Nothing sorts this
+    list. A module that produces a key must sit before the module that consumes it;
+    appending a producer to the end of the list does not work, however clearly its
+    output names say what it feeds. ``find_key_flow_errors`` catches that specific
+    mistake without building anything -- see its docstring for why that matters.
     """
 
     models: List[Any] = field(
@@ -223,6 +229,57 @@ class ModuleContainerConfig:
         metadata={"help": "Output tensor keys produced by this container."},
     )
 
+    def find_key_flow_errors(self) -> List[str]:
+        """Return descriptions of producer-after-consumer ordering mistakes.
+
+        A module needs a key, no earlier module declares it as an output, and a
+        LATER module does. That is unambiguous: the producer exists, it is simply
+        in the wrong place, and no amount of runtime context can fix it.
+
+        Deliberately narrow. It does NOT report keys that are never produced at
+        all, because a few modules derive their real keys at construction time
+        rather than declaring them in config (``DiscreteLatent`` takes its
+        ``in_keys`` from its encoder, for instance), so "not declared anywhere"
+        does not imply "missing". Restricting the check to keys with a known
+        later producer makes false positives impossible, which is what lets it run
+        over every experiment config in the repo.
+
+        The value is that this needs no torch, no lazy-layer materialization, no
+        GPU and no environment. ``ModuleContainer.__init__`` performs the
+        authoritative check, but only while building the modules -- so a config
+        that is wrong in this way otherwise gets discovered at model-construction
+        time, which in a distributed job means one rank dying and the rest
+        deadlocking at the next collective.
+
+        Returns:
+            One human-readable string per mistake found; empty when the ordering
+            is sound.
+        """
+        produced_at: Dict[str, int] = {}
+        for index, model in enumerate(self.models):
+            for key in getattr(model, "out_keys", None) or []:
+                produced_at.setdefault(key, index)
+
+        errors: List[str] = []
+        available = set(self.in_keys)
+        for index, model in enumerate(self.models):
+            name = type(model).__name__
+            for key in getattr(model, "in_keys", None) or []:
+                if key in available:
+                    continue
+                producer = produced_at.get(key)
+                if producer is not None and producer > index:
+                    producer_name = type(self.models[producer]).__name__
+                    errors.append(
+                        f"model {index} ({name}) needs input key {key!r}, but the "
+                        f"module that produces it is model {producer} "
+                        f"({producer_name}), which runs later. `models` is "
+                        f"execution order: move the producer before the consumer."
+                    )
+            for key in getattr(model, "out_keys", None) or []:
+                available.add(key)
+        return errors
+
 
 @dataclass
 class PretrainedModelConfig:
diff --git a/protomotions/agents/evaluators/config.py b/protomotions/agents/evaluators/config.py
index 15e584930..57be066f3 100644
--- a/protomotions/agents/evaluators/config.py
+++ b/protomotions/agents/evaluators/config.py
@@ -59,17 +59,3 @@ class MimicEvaluatorConfig(EvaluatorConfig):
         default_factory=MotionWeightsRulesConfig,
         metadata={"help": "Rules for updating motion sampling weights."}
     )
-    eval_action_ema_alpha: Optional[float] = field(
-        default=None,
-        metadata={
-            "help": (
-                "EMA smoothing factor for actions during evaluation only. "
-                "Simulates deployment low-pass filtering. "
-                "a_applied = alpha * a_policy + (1-alpha) * a_prev. "
-                "None = disabled (raw actions). Typical values: 0.5-0.8."
-                "Smaller alpha = more smoothing."
-            ),
-            "min": 0.0,
-            "max": 1.0,
-        }
-    )
diff --git a/protomotions/agents/evaluators/mimic_evaluator.py b/protomotions/agents/evaluators/mimic_evaluator.py
index 793e0e8dd..56748b792 100644
--- a/protomotions/agents/evaluators/mimic_evaluator.py
+++ b/protomotions/agents/evaluators/mimic_evaluator.py
@@ -70,15 +70,22 @@ def _create_metrics(
         return metrics
 
     def initialize_eval(self) -> Dict:
-        """Initialize evaluation tracking and cache env state for restoration."""
+        """Initialize evaluation tracking and cache the state eval will overwrite.
+
+        Saves the env state, the model's rollout context, and the motion manager's
+        clip assignment, all restored by ``cleanup_after_evaluation``.
+        """
         num_motions = self.motion_lib.num_motions()
         motion_lengths = self.motion_lib.get_motion_length(None)
         motion_num_frames = (motion_lengths / self.env.dt).floor().long()
         motion_num_frames = motion_num_frames.clamp(max=self.config.max_eval_steps)
         self._init_eval_component_buffers(num_motions)
 
-        # Cache env + motion manager state (restored in cleanup_after_evaluation)
+        # Cache env, model rollout context, and motion manager state (restored in
+        # cleanup_after_evaluation). Evaluation reseeds the model context of every
+        # env it resets, so training would otherwise resume with eval's context.
         self._env_snapshot = self.env.save_state()
+        self._model_context_snapshot = self.agent.model.save_rollout_context()
         self._cached_motion_ids = self.motion_manager.motion_ids.clone()
         self._cached_motion_times = self.motion_manager.motion_times.clone()
 
@@ -143,70 +150,51 @@ def _resolved_min_motion_weight(self, num_motions: int) -> float:
             return float(min_weight)
         return float(min_weight)
 
-    def _park_inactive_envs(self, active_env_ids: Tensor) -> None:
-        """Move envs not in ``active_env_ids`` far below the terrain.
-
-        With scene-paired motions, ``_build_eval_batches`` returns a single
-        batch whose ``env_ids`` (from ``get_unique_fixed_motions``) can be
-        much smaller than ``num_envs`` -- the remaining envs would otherwise
-        keep running physics with their (replicated) scenes, contributing
-        substantially to the PhysX broadphase pair budget and triggering
-        ``foundLostPairsCapacity`` overflow at large num_envs (silent contact
-        drops -> tunneling -> phantom failures).
-
-        Parking those envs at z << 0 removes their AABBs from the broadphase
-        active region without changing the policy's view of the rollout.
-        """
-        if active_env_ids is None or active_env_ids.numel() >= self.num_envs:
-            return
-        all_mask = torch.ones(self.num_envs, dtype=torch.bool, device=self.device)
-        all_mask[active_env_ids] = False
-        inactive_env_ids = torch.nonzero(all_mask, as_tuple=False).flatten()
-        if inactive_env_ids.numel() == 0:
-            return
-        self.env.simulator.park_envs(inactive_env_ids)
-
     def evaluate_episode(self, env_ids: torch.Tensor, max_steps: int) -> None:
-        """Run a single episode batch, optionally with EMA action smoothing.
-
-        When eval_action_ema_alpha is set, actions are low-pass filtered to
-        simulate deployment conditions. Motions that fail under EMA get higher
-        sampling weight, creating curriculum pressure toward smooth policies.
+        """Run a single episode batch with the policy's own actions.
+
+        Evaluation is an ordinary rollout in which only ``env_ids`` are scored. Every
+        other env plays its background clip (see ``_background_motion_ids``) and, like
+        a training env, restarts it whenever it falls or the clip ends. No env is
+        parked or left idle: all envs share one physics scene, so the unscored ones
+        should load it the way training does. Parking them below the floor instead
+        cost ~12.8 points of SAMP eval success at 8192 envs (75.96% vs 88.80%).
+        Scoring stays on the deduped ``env_ids`` subset -- widening it would corrupt
+        the motion-id-indexed buffers (duplicate indices drop writes).
+
+        Every env reset here, scored or not, also gets fresh model rollout context
+        (for example MaskedMimic's per-episode latent noise), as training does after
+        an env finishes. ``env.reset`` already clears the env's own history.
         """
-        ema_alpha = self.config.eval_action_ema_alpha
-
         self._on_episode_start(env_ids)
-
-        # Park envs that aren't part of this batch so they don't generate
-        # PhysX broadphase pairs / contacts during the eval. Pre-eval state is
-        # restored later in cleanup_after_evaluation via env.restore_state().
-        self._park_inactive_envs(env_ids)
-
-        obs, _ = self.env.reset(env_ids, **self._get_reset_kwargs())
+        all_env_ids = torch.arange(self.num_envs, device=self.device)
+        obs, _ = self.env.reset(all_env_ids, **self._get_reset_kwargs())
+        self.agent.model.reset_rollout_context(env_ids=all_env_ids)
+        unscored = torch.ones(self.num_envs, dtype=torch.bool, device=self.device)
+        unscored[env_ids] = False
         self.agent.pre_collect_step(0)
         obs = self.agent.add_agent_info_to_obs(obs)
         obs_td = self.agent.obs_dict_to_tensordict(obs)
 
-        prev_actions = None
-
         for step_idx in range(max_steps):
             actions = self._policy_action(obs_td)
+            obs, rewards, dones, terminated, extras = self.env.step(actions)
 
-            # Apply EMA smoothing (deployment simulation)
-            if ema_alpha is not None:
-                if prev_actions is None:
-                    prev_actions = actions.clone()
-                actions = ema_alpha * actions + (1.0 - ema_alpha) * prev_actions
-                prev_actions = actions.clone()
+            # Score before restarting anything: a reset discards the cached env
+            # context that the failure checks read.
+            self._check_eval_components(env_ids, step_idx)
+            self._on_episode_step(env_ids, extras, actions)
+
+            restart_ids = (dones.bool() & unscored).nonzero(as_tuple=False).flatten()
+            if restart_ids.numel() > 0:
+                self.motion_manager.motion_times[restart_ids] = 0.0
+                obs, _ = self.env.reset(restart_ids, **self._get_reset_kwargs())
+                self.agent.model.reset_rollout_context(env_ids=restart_ids)
 
-            obs, rewards, dones, terminated, extras = self.env.step(actions)
             self.agent.pre_collect_step(step_idx + 1)
             obs = self.agent.add_agent_info_to_obs(obs)
             obs_td = self.agent.obs_dict_to_tensordict(obs)
 
-            self._check_eval_components(env_ids, step_idx)
-            self._on_episode_step(env_ids, extras, actions)
-
     def run_evaluation(self) -> None:
         """Run evaluation across multiple motions."""
         for env_ids, motion_ids in self._build_eval_batches():
@@ -251,10 +239,30 @@ def _build_eval_batches(self):
 
     # --- Hook overrides ---
 
+    def _background_motion_ids(self) -> Tensor:
+        """The clip each env plays while it is not being scored.
+
+        An env with a fixed (scene-paired) clip plays that clip, the only one its
+        scene objects match. Any other env plays library clip ``env_id % num_motions``:
+        a fixed choice rather than a random draw, so evaluation stays reproducible,
+        and one that spreads the unscored envs over different clips.
+        """
+        env_ids = torch.arange(self.num_envs, device=self.device)
+        motion_ids = env_ids % self.motion_lib.num_motions()
+        fixed = getattr(self.motion_manager, "_fixed_motion_ids_per_env", None)
+        if fixed is not None:
+            motion_ids = torch.where(fixed != -1, fixed, motion_ids)
+        return motion_ids
+
     def _on_episode_start(self, env_ids: Tensor) -> None:
-        """Set motion_ids/times in the motion manager before reset."""
+        """Set motion_ids/times in the motion manager before reset.
+
+        Every env starts its background clip from frame 0, then the scored
+        subset is overlaid with this batch's motions.
+        """
+        self.motion_manager.motion_ids[:] = self._background_motion_ids()
+        self.motion_manager.motion_times[:] = 0.0
         self.motion_manager.motion_ids[env_ids] = self._episode_ctx.motion_ids
-        self.motion_manager.motion_times[env_ids] = 0.0
 
     def _get_reset_kwargs(self) -> dict:
         """Customize env.reset() for mimic evaluation."""
@@ -316,12 +324,19 @@ def process_eval_results(self) -> Tuple[Dict, Optional[float], int]:
         return to_log, success_rate, num_eval_items
 
     def cleanup_after_evaluation(self) -> None:
-        """Restore env and motion manager state after evaluation."""
+        """Restore the env, model rollout context, and motion manager state.
+
+        ``env.restore_state`` first clears the simulator's contact memory (see
+        ``Simulator.flush_contact_cache``), then writes every env's saved state
+        back, so training continues the episodes it was running before eval.
+        """
         self.motion_manager.motion_ids = self._cached_motion_ids
         self.motion_manager.motion_times = self._cached_motion_times
         self.env.restore_state(self._env_snapshot)
+        self.agent.model.load_rollout_context(self._model_context_snapshot)
 
         del self._env_snapshot
+        del self._model_context_snapshot
         del self._cached_motion_ids
         del self._cached_motion_times
         super().cleanup_after_evaluation()
diff --git a/protomotions/envs/action/action_functions.py b/protomotions/envs/action/action_functions.py
index 2eae2cef5..5c1600e77 100644
--- a/protomotions/envs/action/action_functions.py
+++ b/protomotions/envs/action/action_functions.py
@@ -359,9 +359,9 @@ def bm_pd_action(
 def make_bm_pd_action_config(robot_config) -> Dict[str, Any]:
     """Create action config dict for BeyondMimic-style PD action scaling.
 
-    Computes per-joint action scale as ``effort_limit / stiffness``,
-    which normalizes the policy output so ~O(1) values map to ~ the
-    maximum achievable deflection per joint. Uses ``robot_config.default_dof_pos``
+    Computes per-joint action scale as ``0.25 * effort_limit / stiffness``,
+    so unit policy output produces a proportional torque of 25% of the effort
+    limit at the default pose. Uses ``robot_config.default_dof_pos``
     as the action offset (default standing pose).
 
     Args:
@@ -391,7 +391,7 @@ def make_bm_pd_action_config(robot_config) -> Dict[str, Any]:
     if torch.any(stiffness <= 0):
         raise ValueError("BM PD action stiffness must be positive for all joints")
 
-    action_scale = effort_limit / stiffness
+    action_scale = 0.25 * effort_limit / stiffness
 
     pd_action_offset = robot_config.default_dof_pos.clone()
 
diff --git a/protomotions/envs/base_env/env.py b/protomotions/envs/base_env/env.py
index 5f8d143c1..ac57439b0 100644
--- a/protomotions/envs/base_env/env.py
+++ b/protomotions/envs/base_env/env.py
@@ -984,6 +984,7 @@ def _build_context(self, env_ids: Optional[Tensor] = None) -> EnvContext:
             if self.state_history
             else None,
             # Actions (historical)
+            current_action=self._select_context_tensor(self._current_raw_action, env_ids),
             current_processed_action=self._select_context_tensor(
                 self._current_processed_action, env_ids
             ),
@@ -1901,6 +1902,13 @@ def restore_state(self, snapshot: dict) -> None:
             snapshot: Dictionary from save_state() containing state tensors
         """
         env_ids = torch.arange(self.num_envs, device=self.device)
+        # Every env is about to be teleported back. Clear the simulator's
+        # memory of the contacts at the current poses first: otherwise a robot
+        # that was standing on the ground here can be launched on the first
+        # physics substep after the write-back.
+        flush_contact_cache = getattr(self.simulator, "flush_contact_cache", None)
+        if flush_contact_cache is not None:
+            flush_contact_cache()
         self.simulator.reset_envs(
             snapshot["robot_state"], snapshot.get("object_state"), env_ids
         )
diff --git a/protomotions/envs/base_env/utils.py b/protomotions/envs/base_env/utils.py
index 71e63aeb1..a16b004b9 100644
--- a/protomotions/envs/base_env/utils.py
+++ b/protomotions/envs/base_env/utils.py
@@ -166,7 +166,8 @@ def combine_evaluation(
     Evaluation components return numeric values [num_envs]. If a component has
     a ``threshold`` in its static_params, the value is compared against it to
     determine failure. ``fail_above`` (default True) controls the comparison
-    direction.
+    direction. Evaluation kernels must not declare a ``threshold`` parameter,
+    otherwise MdpComponent would pass this failure cut to the kernel as well.
 
     Args:
         raw_values: Dict of {name: value_tensor} from ComponentManager.execute_all().
diff --git a/protomotions/envs/component_factories.py b/protomotions/envs/component_factories.py
index 009500554..1e6155acf 100644
--- a/protomotions/envs/component_factories.py
+++ b/protomotions/envs/component_factories.py
@@ -758,6 +758,20 @@ def action_smoothness_factory(weight: float = -0.02) -> MdpComponent:
     )
 
 
+def action_rate_l2_factory(weight: float = -0.1) -> MdpComponent:
+    """Penalize squared raw action changes; requires at least one history step."""
+    from protomotions.envs.rewards import compute_action_rate_l2
+
+    return MdpComponent(
+        compute_func=compute_action_rate_l2,
+        dynamic_vars={
+            "raw_action": EnvContext.current_action,
+            "previous_raw_action": EnvContext.previous_action,
+        },
+        static_params={"weight": weight},
+    )
+
+
 def gt_rew_factory(weight: float = 0.5, coefficient: float = -100.0) -> MdpComponent:
     """Factory for position tracking reward.
 
@@ -1813,6 +1827,7 @@ def steering_velocity_error_factory(
     "path_obs_factory",
     # Reward factories
     "action_smoothness_factory",
+    "action_rate_l2_factory",
     "gt_rew_factory",
     "gr_rew_factory",
     "gv_rew_factory",
diff --git a/protomotions/envs/context_views.py b/protomotions/envs/context_views.py
index 58d2e93bc..de96f720d 100644
--- a/protomotions/envs/context_views.py
+++ b/protomotions/envs/context_views.py
@@ -144,6 +144,16 @@ def __init__(self, state: "RobotState", anchor_idx: int):
         self.anchor_vel = state.rigid_body_vel[:, anchor_idx, :]
         self.anchor_ang_vel = state.rigid_body_ang_vel[:, anchor_idx, :]
 
+        # NoisyObservations also carries root/anchor channels that were noised
+        # on their own (anchor_rot_noise, root_ang_vel_noise,
+        # anchor_ang_vel_noise). Re-deriving them from the whole-body tensors
+        # would silently drop that noise, so use them when present. RobotState
+        # has none of these fields, so clean views are unchanged.
+        for name in ("anchor_rot", "root_local_ang_vel", "anchor_local_ang_vel"):
+            value = getattr(state, name, None)
+            if value is not None:
+                setattr(self, name, value)
+
     def _compute_root_local_ang_vel(self) -> Tensor:
         return compute_local_ang_vel(self.root_rot, self.root_ang_vel)
 
@@ -591,6 +601,7 @@ class EnvContext:
     noisy_historical: Optional[HistoricalView] = NestedField(HistoricalView)
 
     # Actions (historical)
+    current_action: Optional[Tensor] = FieldPath()
     previous_action: Optional[Tensor] = FieldPath()
     current_processed_action: Optional[Tensor] = FieldPath()
     previous_processed_action: Optional[Tensor] = FieldPath()
@@ -645,6 +656,7 @@ def __init__(
         dt: float,
         historical: Optional[HistoricalView] = None,
         noisy_historical: Optional[HistoricalView] = None,
+        current_action: Optional[Tensor] = None,
         previous_action: Optional[Tensor] = None,
         current_processed_action: Optional[Tensor] = None,
         previous_processed_action: Optional[Tensor] = None,
@@ -679,6 +691,7 @@ def __init__(
             dt: Simulation timestep in seconds.
             historical: Clean historical state view (optional).
             noisy_historical: Noisy historical state view (optional).
+            current_action: Current raw policy action before PD processing (optional).
             previous_action: Previous raw action [num_envs, action_dim] (optional).
             current_processed_action: Current processed action (optional).
             previous_processed_action: Previous processed action (optional).
@@ -711,6 +724,7 @@ def __init__(
         self.noisy_historical = noisy_historical
 
         # Actions
+        self.current_action = current_action
         self.previous_action = previous_action
         self.current_processed_action = current_processed_action
         self.previous_processed_action = previous_processed_action
diff --git a/protomotions/envs/mdp_component.py b/protomotions/envs/mdp_component.py
index cfd909a1f..09a2a6630 100644
--- a/protomotions/envs/mdp_component.py
+++ b/protomotions/envs/mdp_component.py
@@ -38,12 +38,20 @@
 
 Metadata keys
 -------------
-Some ``static_params`` keys are *metadata* consumed by the reward/termination
-combining logic, not by the compute_func itself.  These are automatically filtered
-out before calling the compute_func.  Current metadata keys:
+Some ``static_params`` keys are *metadata* read by the combining logic in
+``base_env/utils.py`` (reward weighting and clamping, the evaluation failure
+cut).  Current metadata keys:
 
     weight, multiplicative, zero_during_grace_period,
-    min_value, max_value
+    min_value, max_value, threshold, fail_above, use_region_weights
+
+A metadata key is passed to the compute_func only when the compute_func
+declares a parameter with that name; otherwise it is filtered out.  Termination
+kernels declare ``threshold``, so a termination's configured threshold reaches
+its kernel.  Evaluation kernels must not declare ``threshold``: for them it is
+the failure cut that ``combine_evaluation`` applies to the kernel's output.  A
+compute_func that only takes ``**kwargs``, or whose signature cannot be
+inspected (a TorchScript function, for example), receives no metadata keys.
 
 Example
 -------
@@ -80,7 +88,8 @@
 
 from __future__ import annotations
 
-from typing import Any, Callable, Dict, TYPE_CHECKING
+import inspect
+from typing import Any, Callable, Dict, FrozenSet, TYPE_CHECKING
 
 from torch import Tensor
 
@@ -90,9 +99,9 @@
     from protomotions.envs.context_views import EnvContext
 
 
-# Keys in static_params that are reward/termination metadata, NOT compute_func arguments.
-# These are consumed by combine_rewards / combine_terminations and must not
-# be forwarded to the compute_func function.
+# Keys in static_params that are read by combine_rewards / combine_evaluation.
+# resolve_args drops them before calling the compute_func, unless the
+# compute_func declares a parameter of the same name.
 _METADATA_KEYS = frozenset({
     "weight", "multiplicative", "zero_during_grace_period",
     "min_value", "max_value",
@@ -100,6 +109,27 @@
 })
 
 
+def _declared_keyword_params(func: Callable[..., Any]) -> FrozenSet[str]:
+    """Return the names of the parameters ``func`` declares and accepts by keyword.
+
+    ``*args`` and ``**kwargs`` do not count, so a wrapper that forwards
+    ``**kwargs`` declares nothing. A function whose signature cannot be
+    inspected declares nothing either. That includes TorchScript functions, so a
+    ``@torch.jit.script`` kernel never receives metadata keys: if one needs a
+    threshold, give its parameter a name that is not in ``_METADATA_KEYS``.
+    """
+    try:
+        parameters = inspect.signature(func).parameters.values()
+    except (TypeError, ValueError):
+        return frozenset()
+    return frozenset(
+        param.name
+        for param in parameters
+        if param.kind
+        in (inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.KEYWORD_ONLY)
+    )
+
+
 class MdpComponent:
     """A compute function with explicit context bindings.
     
@@ -157,15 +187,18 @@ def __init__(
         self.static_params = static_params or {}
         self.compile = compile
         self._device_ready = False
+        self._metadata_args = None
 
     def __getstate__(self):
         state = self.__dict__.copy()
         state.pop("_device_ready", None)
+        state.pop("_metadata_args", None)
         return state
 
     def __setstate__(self, state):
         self.__dict__.update(state)
         self._device_ready = False
+        self._metadata_args = None
 
     
     def resolve_args(self, ctx: "EnvContext") -> tuple:
@@ -188,9 +221,15 @@ def resolve_args(self, ctx: "EnvContext") -> tuple:
         if not self._device_ready:
             self._ensure_device(resolved)
 
+        if self._metadata_args is None:
+            # Metadata keys the compute_func asks for by name, such as a
+            # termination kernel's threshold.
+            self._metadata_args = _METADATA_KEYS & _declared_keyword_params(
+                self.compute_func
+            )
         func_params = {
             k: v for k, v in self.static_params.items()
-            if k not in _METADATA_KEYS
+            if k not in _METADATA_KEYS or k in self._metadata_args
         }
         return resolved, func_params
 
diff --git a/protomotions/envs/rewards/__init__.py b/protomotions/envs/rewards/__init__.py
index ce9507093..2b69c8f96 100644
--- a/protomotions/envs/rewards/__init__.py
+++ b/protomotions/envs/rewards/__init__.py
@@ -63,6 +63,7 @@
 # Regularization reward kernels
 from protomotions.envs.rewards.regularization import (
     compute_action_smoothness,
+    compute_action_rate_l2,
     compute_action_smoothness_logmeanexp,
     compute_pow_rew,
     compute_soft_pos_limit_rew,
@@ -113,6 +114,7 @@
     "compute_target_rew",
     # Regularization reward kernels
     "compute_action_smoothness",
+    "compute_action_rate_l2",
     "compute_action_smoothness_logmeanexp",
     "compute_pow_rew",
     "compute_soft_pos_limit_rew",
diff --git a/protomotions/envs/rewards/regularization.py b/protomotions/envs/rewards/regularization.py
index d7b1de659..704bdefb7 100644
--- a/protomotions/envs/rewards/regularization.py
+++ b/protomotions/envs/rewards/regularization.py
@@ -57,6 +57,15 @@ def compute_action_smoothness(
     return delta_norm(current_processed_action, previous_processed_action)
 
 
+def compute_action_rate_l2(raw_action: Tensor, previous_raw_action: Tensor) -> Tensor:
+    """Sum squared raw policy-action changes over joints, per environment.
+
+    Inputs are dimensionless policy outputs [num_envs, action_dim], before
+    clipping or PD scaling. No mean, square root, or timestep scaling is applied.
+    """
+    return (raw_action - previous_raw_action).square().sum(dim=-1)
+
+
 def compute_action_smoothness_logmeanexp(
     current_processed_action: Tensor,
     previous_processed_action: Tensor,
diff --git a/protomotions/inference_agent.py b/protomotions/inference_agent.py
index aa05b659f..f58855fae 100644
--- a/protomotions/inference_agent.py
+++ b/protomotions/inference_agent.py
@@ -267,10 +267,12 @@ def main():
         -3
     ]  # e.g., "isaacgym" from "protomotions.simulator.isaacgym.simulator.IsaacGymSimulator"
 
+    train_sim_params = None
     if args.simulator != current_simulator:
         log.info(
             f"Switching simulator from '{current_simulator}' (training) to '{args.simulator}' (inference)"
         )
+        train_sim_params = simulator_config.sim
         from protomotions.simulator.factory import update_simulator_config_for_test
 
         simulator_config = update_simulator_config_for_test(
@@ -341,6 +343,13 @@ def main():
         log.info(f"CLI override: command_source = {args.command_source}")
         apply_command_source_overrides(env_config, args.command_source)
 
+    if train_sim_params is not None:
+        from protomotions.simulator.factory import warn_on_control_rate_change
+
+        warn_on_control_rate_change(
+            train_sim_params, simulator_config.sim, args.simulator
+        )
+
     motion_lib_config.validate()
 
     # Create fabric config for inference (simplified)
diff --git a/protomotions/robot_configs/amp.py b/protomotions/robot_configs/amp.py
index 215d8fdfd..8ca9ed939 100644
--- a/protomotions/robot_configs/amp.py
+++ b/protomotions/robot_configs/amp.py
@@ -12,6 +12,7 @@
 from protomotions.simulator.isaaclab.config import IsaacLabSimParams
 from protomotions.simulator.genesis.config import GenesisSimParams
 from protomotions.simulator.newton.config import NewtonSimParams
+from protomotions.simulator.mujoco.config import MujocoSimParams
 from typing import List, Dict, Tuple
 from dataclasses import dataclass, field
 
@@ -74,5 +75,13 @@ class AMPRobotConfig(RobotConfig):
                 fps=120,
                 decimation=4,
             ),
+            # Same 30 Hz control rate as the other simulators. implicitfast
+            # also treats the PD damping implicitly (Euler only does that for
+            # joint damping), which keeps stiff gains on light links stable.
+            mujoco=MujocoSimParams(
+                fps=960,
+                decimation=32,
+                integrator="implicitfast",
+            ),
         )
     )
diff --git a/protomotions/robot_configs/smpl.py b/protomotions/robot_configs/smpl.py
index 6b652a142..43ae804bc 100644
--- a/protomotions/robot_configs/smpl.py
+++ b/protomotions/robot_configs/smpl.py
@@ -15,6 +15,7 @@
 )
 from protomotions.simulator.genesis.config import GenesisSimParams
 from protomotions.simulator.newton.config import NewtonSimParams
+from protomotions.simulator.mujoco.config import MujocoSimParams
 from protomotions.components.pose_lib import ControlInfo
 from typing import List, Dict, Tuple
 from dataclasses import dataclass, field
@@ -123,5 +124,13 @@ class SmplRobotConfig(RobotConfig):
                 fps=120,
                 decimation=4,
             ),
+            # Same 30 Hz control rate as the other simulators. implicitfast
+            # also treats the PD damping implicitly (Euler only does that for
+            # joint damping), which keeps stiff gains on light links stable.
+            mujoco=MujocoSimParams(
+                fps=960,
+                decimation=32,
+                integrator="implicitfast",
+            ),
         )
     )
diff --git a/protomotions/robot_configs/smplx.py b/protomotions/robot_configs/smplx.py
index 81d9124d5..8ebf74c6c 100644
--- a/protomotions/robot_configs/smplx.py
+++ b/protomotions/robot_configs/smplx.py
@@ -15,6 +15,7 @@
 )
 from protomotions.simulator.genesis.config import GenesisSimParams
 from protomotions.simulator.newton.config import NewtonSimParams
+from protomotions.simulator.mujoco.config import MujocoSimParams
 from protomotions.components.pose_lib import ControlInfo
 from typing import List, Dict, Tuple
 from dataclasses import dataclass, field
@@ -208,5 +209,13 @@ class SMPLXRobotConfig(RobotConfig):
                 fps=120,
                 decimation=4,
             ),
+            # Same 30 Hz control rate as the other simulators. implicitfast
+            # also treats the PD damping implicitly (Euler only does that for
+            # joint damping), which keeps stiff gains on light links stable.
+            mujoco=MujocoSimParams(
+                fps=960,
+                decimation=32,
+                integrator="implicitfast",
+            ),
         )
     )
diff --git a/protomotions/robot_configs/soma23.py b/protomotions/robot_configs/soma23.py
index fa042c258..b225ab3c2 100644
--- a/protomotions/robot_configs/soma23.py
+++ b/protomotions/robot_configs/soma23.py
@@ -15,6 +15,7 @@
 )
 from protomotions.simulator.genesis.config import GenesisSimParams
 from protomotions.simulator.newton.config import NewtonSimParams
+from protomotions.simulator.mujoco.config import MujocoSimParams
 from protomotions.components.pose_lib import ControlInfo
 from typing import List, Dict, Tuple
 from dataclasses import dataclass, field
@@ -118,5 +119,13 @@ class Soma23RobotConfig(RobotConfig):
                 fps=120,
                 decimation=4,
             ),
+            # Same 30 Hz control rate as the other simulators. implicitfast
+            # also treats the PD damping implicitly (Euler only does that for
+            # joint damping), which keeps stiff gains on light links stable.
+            mujoco=MujocoSimParams(
+                fps=960,
+                decimation=32,
+                integrator="implicitfast",
+            ),
         )
     )
diff --git a/protomotions/simulator/base_simulator/robot_mass_properties.py b/protomotions/simulator/base_simulator/robot_mass_properties.py
new file mode 100644
index 000000000..4b29a6bc2
--- /dev/null
+++ b/protomotions/simulator/base_simulator/robot_mass_properties.py
@@ -0,0 +1,127 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Normalize mass and inertia of massless robot links before backend import."""
+
+from copy import deepcopy
+from pathlib import Path
+import xml.etree.ElementTree as ET
+
+from dm_control import mjcf
+import mujoco
+
+
+MARKER_MASS = 0.001  # kg
+MARKER_INERTIA = 4e-8  # kg m^2: a one-gram, one-centimetre-radius sphere
+
+
+def _read_mjcf(path):
+    """Expand includes and make external asset references location independent."""
+    path = Path(path).resolve()
+    root = ET.parse(path).getroot()
+
+    def expand(parent, ancestors):
+        for child in list(parent):
+            if child.tag != "include":
+                expand(child, ancestors)
+                continue
+            included_path = (path.parent / child.attrib["file"]).resolve()
+            if included_path in ancestors:
+                raise ValueError(f"Recursive MJCF include: {included_path}")
+            included = ET.parse(included_path).getroot()
+            expand(included, ancestors | {included_path})
+            index = list(parent).index(child)
+            parent.remove(child)
+            for offset, element in enumerate(included):
+                parent.insert(index + offset, element)
+
+    expand(root, {path})
+    compiler = root.find("compiler")
+    options = compiler.attrib if compiler is not None else {}
+    for element in root.findall("asset/*"):
+        for attribute in (
+            "file",
+            "fileleft",
+            "fileright",
+            "filefront",
+            "fileback",
+            "fileup",
+            "filedown",
+        ):
+            filename = element.get(attribute)
+            if not filename:
+                continue
+            directory_key = {"mesh": "meshdir", "texture": "texturedir"}.get(
+                element.tag, "assetdir"
+            )
+            directory = options.get(directory_key, options.get("assetdir", ""))
+            if options.get("strippath") == "true":
+                filename = Path(filename).name
+            element.set(attribute, str((path.parent / directory / filename).resolve()))
+    if compiler is not None:
+        for attribute in ("meshdir", "texturedir", "assetdir", "strippath"):
+            compiler.attrib.pop(attribute, None)
+    return root
+
+
+def prepare_robot_mjcf(mjcf_path, output_dir):
+    """Return an importable MJCF with one-gram inertias for zero-mass bodies.
+
+    The source is never edited. MuJoCo resolves defaults, geometry-derived mass
+    and includes, rather than guessing from missing XML attributes. Only robot
+    inertia is inspected; references to runtime sensors/ground are excluded from
+    the probe, not from the resulting asset. Assets with no massless links use
+    their original path. The caller owns the temporary output directory.
+    """
+    root = _read_mjcf(mjcf_path)
+    probe = deepcopy(root)
+    for tag in ("sensor", "contact", "actuator", "tendon", "equality", "keyframe"):
+        for element in probe.findall(tag):
+            probe.remove(element)
+    compiler = probe.find("compiler")
+    if compiler is None:
+        compiler = ET.SubElement(probe, "compiler")
+    compiler.set("fusestatic", "false")
+    source = mjcf.from_xml_string(ET.tostring(probe, encoding="unicode"))
+    model = mujoco.MjModel.from_xml_string(source.to_xml_string(), source.get_assets())
+    massless = {i for i in range(1, model.nbody) if model.body_mass[i] == 0}
+    if not massless:
+        return str(mjcf_path)
+
+    compiler = root.find("compiler")
+    infer_from_geometry = (
+        compiler is not None and compiler.get("inertiafromgeom") == "true"
+    )
+    # With static fusion disabled, XML preorder is MuJoCo body order, including
+    # unnamed bodies. Body names and ordering are never rewritten.
+    for body_id, body in enumerate(root.findall(".//worldbody//body"), start=1):
+        if body_id not in massless and not infer_from_geometry:
+            continue
+        properties = model.body(body_id)
+        inertial = body.find("inertial")
+        if inertial is not None:
+            body.remove(inertial)
+        if body_id in massless:
+            attributes = dict(
+                pos="0 0 0",
+                mass=str(MARKER_MASS),
+                diaginertia=" ".join([str(MARKER_INERTIA)] * 3),
+            )
+        else:
+            # Preserve geometry-derived physical properties when switching the
+            # compiler from forced inference to explicit/automatic inertia.
+            attributes = dict(
+                pos=" ".join(map(str, properties.ipos)),
+                quat=" ".join(map(str, properties.iquat)),
+                mass=str(properties.mass[0]),
+                diaginertia=" ".join(map(str, properties.inertia)),
+            )
+        body.insert(0, ET.Element("inertial", attributes))
+    if infer_from_geometry:
+        compiler.set("inertiafromgeom", "auto")
+
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    output_path = output_dir / Path(mjcf_path).name
+    output_path.write_text(ET.tostring(root, encoding="unicode"))
+    return str(output_path)
diff --git a/protomotions/simulator/base_simulator/simulator.py b/protomotions/simulator/base_simulator/simulator.py
index 6a852f5d2..7c9f46e01 100644
--- a/protomotions/simulator/base_simulator/simulator.py
+++ b/protomotions/simulator/base_simulator/simulator.py
@@ -23,6 +23,8 @@
 from abc import ABC, abstractmethod
 import logging
 import math
+from pathlib import Path
+from tempfile import TemporaryDirectory
 
 from typing import Dict, List, Optional, Any, Tuple, Callable
 
@@ -55,6 +57,8 @@
 from protomotions.robot_configs.base import ControlType, RobotConfig
 from protomotions.simulator.base_simulator.record import RecordingMixin
 from protomotions.simulator.base_simulator.user_interface import UserInterface
+from protomotions.assets import resolve_asset_root
+from protomotions.simulator.base_simulator.robot_mass_properties import prepare_robot_mjcf
 
 
 class Simulator(RecordingMixin, ABC):
@@ -99,6 +103,23 @@ class Simulator(RecordingMixin, ABC):
     # -------------------------
     # ⚙️ Group 1: Initialization & Configuration
     # -------------------------
+    def _get_robot_asset_path(self) -> str:
+        """Prepare robot inertia once, retaining temporary assets for its lifetime."""
+        if not hasattr(self, "_prepared_robot_asset_path"):
+            path = (
+                resolve_asset_root(self.robot_config.asset.asset_root)
+                / self.robot_config.asset.asset_file_name
+            )
+            self._robot_asset_directory = TemporaryDirectory(
+                prefix="protomotions-robot-"
+            )
+            self._prepared_robot_asset_path = (
+                prepare_robot_mjcf(path, self._robot_asset_directory.name)
+                if Path(path).suffix == ".xml"
+                else str(path)
+            )
+        return self._prepared_robot_asset_path
+
     def __init__(
         self,
         config: SimulatorConfig,
@@ -792,85 +813,85 @@ def reset_envs(
         # Reset projectiles for reset environments
         self._reset_projectiles(env_ids)
 
-    def park_envs(
+    def flush_contact_cache(self) -> None:
+        """Make the simulator forget every env's current contacts.
+
+        Called right before every env's state is written back
+        (``BaseEnv.restore_state``). The base implementation does nothing;
+        backends whose contact solver remembers contacts between steps override
+        it. See ``IsaacLabSimulator.flush_contact_cache`` for why this matters.
+        """
+        return None
+
+    def _move_all_envs_out_of_contact(
         self,
-        env_ids: torch.Tensor,
         hide_z: float = -50.0,
+        object_drop: float = 20.0,
     ) -> None:
-        """Move robot and scene objects for ``env_ids`` far below the terrain.
-
-        Used during evaluation to disable physics for envs that are not being
-        evaluated, eliminating their contribution to the PhysX broadphase pair
-        budget. Parked bodies sit well below any terrain/object AABB so no
-        broadphase pairs are generated, no narrow-phase contacts are computed,
-        and no found/lost pair churn occurs. Velocities are zeroed so the
-        parked bodies stay put.
-
-        Pre-eval state is restored later via ``BaseEnv.restore_state(snapshot)``,
-        which calls ``reset_envs`` over all envs with the saved snapshot.
-
-        Args:
-            env_ids: Environment IDs to park. No-op if empty/None.
-            hide_z: World z-coordinate to teleport robot roots to. Object roots
-                are placed slightly below at ``hide_z - 1.0`` so their AABBs
-                cannot overlap with the parked robot's AABB.
+        """Move every robot and scene object far below the terrain, at rest.
+
+        A helper for ``flush_contact_cache`` overrides, which then simulate one
+        substep here and write the real state back. Robots keep their x, y and
+        rotation, so each stays in its own grid cell instead of all landing at
+        the world origin (which could exceed PhysX's broadphase limits at large
+        env counts); they go to ``z = hide_z`` in the default joint pose with
+        zero velocity. Objects keep their x, y and rotation and go to
+        ``hide_z - object_drop``, far enough below that a robot cannot touch
+        its own furniture. Every object of an env goes to that same height, so
+        two objects that overlap when seen from above (a box on a table) end up
+        inside each other, and if either is dynamic the simulator records a
+        contact between them.
+
+        This goes through ``reset_envs``, so it also clears the action buffers
+        and reschedules pushes and projectiles; the write-back that follows
+        does the same again for every env.
         """
         from protomotions.simulator.base_simulator.simulator_state import (
             StateConversion,
         )
 
-        if env_ids is None or env_ids.numel() == 0:
-            return
-
-        n = env_ids.numel()
+        n = self.num_envs
         device = self.device
+        env_ids = torch.arange(n, device=device)
 
-        # Preserve current root xy so parked envs stay in their own grid cell
-        # (avoids stacking all parked envs at world origin, which could exceed
-        # PhysX broadphase region limits at large num_envs).
         current_root = self.get_root_state(env_ids)
-        park_root_pos = current_root.root_pos.clone()
-        park_root_pos[:, 2] = hide_z
-        park_root_rot = current_root.root_rot.clone()
-        zero_root_vel = torch.zeros((n, 3), device=device, dtype=torch.float32)
-
-        num_dofs = self.robot_config.kinematic_info.num_dofs
-        park_dof_pos = (
+        root_pos = current_root.root_pos.clone()
+        root_pos[:, 2] = hide_z
+        zero_vel = torch.zeros((n, 3), device=device, dtype=torch.float32)
+        dof_pos = (
             self.robot_config.default_dof_pos.unsqueeze(0)
             .repeat(n, 1)
             .to(device=device, dtype=torch.float32)
         )
-        park_dof_vel = torch.zeros((n, num_dofs), device=device, dtype=torch.float32)
-
-        park_state = ResetState(
-            root_pos=park_root_pos,
-            root_rot=park_root_rot,
-            root_vel=zero_root_vel,
-            root_ang_vel=zero_root_vel.clone(),
-            dof_pos=park_dof_pos,
-            dof_vel=park_dof_vel,
+        robot_state = ResetState(
+            root_pos=root_pos,
+            root_rot=current_root.root_rot.clone(),
+            root_vel=zero_vel,
+            root_ang_vel=zero_vel.clone(),
+            dof_pos=dof_pos,
+            dof_vel=torch.zeros_like(dof_pos),
             state_conversion=StateConversion.COMMON,
         )
 
-        park_object_state = None
+        object_state = None
         if self.scene_lib.num_objects_per_scene > 0:
             current_obj = self.get_object_root_state(env_ids)
-            obj_root_pos = current_obj.root_pos.clone()
-            obj_root_pos[..., 2] = hide_z - 1.0
-            obj_root_rot = current_obj.root_rot.clone()
-            m = self.scene_lib.num_objects_per_scene
+            obj_pos = current_obj.root_pos.clone()
+            obj_pos[..., 2] = hide_z - object_drop
             zero_obj_vel = torch.zeros(
-                (n, m, 3), device=device, dtype=torch.float32
+                (n, self.scene_lib.num_objects_per_scene, 3),
+                device=device,
+                dtype=torch.float32,
             )
-            park_object_state = ObjectState(
-                root_pos=obj_root_pos,
-                root_rot=obj_root_rot,
+            object_state = ObjectState(
+                root_pos=obj_pos,
+                root_rot=current_obj.root_rot.clone(),
                 root_vel=zero_obj_vel,
                 root_ang_vel=zero_obj_vel.clone(),
                 state_conversion=StateConversion.COMMON,
             )
 
-        self.reset_envs(park_state, park_object_state, env_ids)
+        self.reset_envs(robot_state, object_state, env_ids)
 
     @abstractmethod
     def _set_simulator_env_state(
diff --git a/protomotions/simulator/factory.py b/protomotions/simulator/factory.py
index 1af29e1b4..25c9e8a9d 100644
--- a/protomotions/simulator/factory.py
+++ b/protomotions/simulator/factory.py
@@ -78,6 +78,27 @@ def simulator_config(
     )
 
 
+def warn_on_control_rate_change(
+    train_sim_params, sim_params, simulator_name: str
+) -> bool:
+    """Warn when inference runs the policy at a different control rate than training.
+
+    The policy acts once per control step (fps / decimation), so a different
+    rate puts it in a regime it never trained in. Call this after all config
+    overrides are applied. Returns True if it warned.
+    """
+    train_rate = train_sim_params.fps / train_sim_params.decimation
+    rate = sim_params.fps / sim_params.decimation
+    if abs(train_rate - rate) <= 1e-6 * train_rate:
+        return False
+    log.warning(
+        f"Control rate changes from {train_rate:g} Hz (training) to {rate:g} Hz "
+        f"({simulator_name}). Match it with "
+        f"--overrides simulator.sim.fps=... simulator.sim.decimation=..."
+    )
+    return True
+
+
 def update_simulator_config_for_test(
     current_simulator_config: SimulatorConfig,
     new_simulator: str,
diff --git a/protomotions/simulator/genesis/simulator.py b/protomotions/simulator/genesis/simulator.py
index d9994fba8..0df68f842 100644
--- a/protomotions/simulator/genesis/simulator.py
+++ b/protomotions/simulator/genesis/simulator.py
@@ -6,7 +6,6 @@
 from typing import Dict, Optional, Tuple
 from easydict import EasyDict
 
-from protomotions.assets import resolve_asset_root
 from protomotions.utils import rotations
 from protomotions.simulator.base_simulator.simulator import Simulator
 from protomotions.simulator.base_simulator.config import (
@@ -126,11 +125,7 @@ def _create_simulation(self) -> None:
     # ===== Group 2: Environment Setup & Configuration =====
     def _create_envs(self) -> None:
         """Creates the simulation environments and loads robot assets."""
-        asset_root = resolve_asset_root(self.robot_config.asset.asset_root)
-        asset_file = self.robot_config.asset.asset_file_name
-
-        asset_path = os.path.join(asset_root, asset_file)
-        asset_root = os.path.dirname(asset_path)
+        asset_path = self._get_robot_asset_path()
         asset_file = os.path.basename(asset_path)
         asset_file_ext = os.path.splitext(asset_file)[1]
 
diff --git a/protomotions/simulator/isaacgym/simulator.py b/protomotions/simulator/isaacgym/simulator.py
index 77ee761f0..7ed2fe901 100644
--- a/protomotions/simulator/isaacgym/simulator.py
+++ b/protomotions/simulator/isaacgym/simulator.py
@@ -12,7 +12,7 @@
 import numpy as np
 from rich.progress import Progress
 import os
-from protomotions.assets import get_asset_root, resolve_asset_root
+from protomotions.assets import get_asset_root
 from protomotions.components.terrains.terrain import Terrain
 from protomotions.components.terrains.config import CombineMode
 from protomotions.components.scene_lib import (
@@ -346,9 +346,7 @@ def _load_humanoid_asset(self):
         Returns:
             Loaded asset handle (opaque gymapi handle)
         """
-        asset_root = resolve_asset_root(self.robot_config.asset.asset_root)
-        asset_file = self.robot_config.asset.asset_file_name
-        asset_path = os.path.join(asset_root, asset_file)
+        asset_path = self._get_robot_asset_path()
         asset_root = os.path.dirname(asset_path)
         asset_file = os.path.basename(asset_path)
 
diff --git a/protomotions/simulator/isaaclab/simulator.py b/protomotions/simulator/isaaclab/simulator.py
index d36d37cbc..ec691038b 100644
--- a/protomotions/simulator/isaaclab/simulator.py
+++ b/protomotions/simulator/isaaclab/simulator.py
@@ -56,6 +56,55 @@
 )
 
 
+def _force_sequential_cloning() -> None:
+    """Make IsaacLab assign scene ``i``'s object to env ``i`` instead of drawing at random.
+
+    This gives the exact mapping only when every scene has one object. Each object
+    slot is spawned as its own group, and IsaacLab 3 enumerates combinations of the
+    groups' prototypes with the last group varying fastest, so with two objects per
+    scene env ``i`` gets combination ``(i // N, i % N)`` for ``N`` scenes: the last
+    slot is right, but every earlier slot holds scene 0's object in every env. That
+    is still better than the random default, which scrambles every slot.
+
+    IsaacLab 3 picks which spawn prototype each env receives via a pluggable strategy,
+    and the default is ``random`` -- uniform sampling WITH REPLACEMENT. IsaacLab 2 used a
+    plain round-robin, which for one-prototype-per-env is the identity map ProtoMotions
+    relies on: env i must receive scene i's furniture, because env i is tracking the motion
+    authored against it. Under the random default the furniture is scrambled -- several envs
+    get the same mesh and others get none -- while positions, physics properties and the
+    motion are still applied per env index. The result looks like a plausible scene
+    containing the wrong object, and nothing warns.
+
+    ``InteractiveScene`` builds its own ``CloneCfg`` and offers no injection point. Patching
+    ``CloneCfg`` does not work either: ``configclass`` turns the default into a
+    ``default_factory`` closure captured at class-creation time, so neither the class
+    attribute nor the field default is consulted afterwards. What ``InteractiveScene`` does
+    do is call ``cloner.ReplicateSession(...)`` by module-attribute lookup at call time --
+    so wrapping that class is the one hook that takes effect.
+    """
+    try:
+        import isaaclab.cloner as _cloner
+        from isaaclab.cloner.cloner_strategies import sequential
+    except ImportError:  # pragma: no cover - older IsaacLab has no pluggable strategy
+        return
+
+    session_cls = _cloner.ReplicateSession
+    if getattr(session_cls, "_protomotions_sequential", False):
+        return
+
+    class _SequentialReplicateSession(session_cls):
+        _protomotions_sequential = True
+
+        def __init__(self, *args, **kwargs):
+            kwargs["clone_strategy"] = sequential
+            super().__init__(*args, **kwargs)
+
+    _cloner.ReplicateSession = _SequentialReplicateSession
+    log.info(
+        "Forced IsaacLab clone strategy to 'sequential' (IsaacLab 3 defaults to 'random')."
+    )
+
+
 class IsaacLabSimulator(Simulator):
     config: IsaacLabSimulatorConfig
 
@@ -125,6 +174,9 @@ def __init__(
 
         scene_cfg = self._get_scene_cfg()
 
+        # IsaacLab 3 defaults to a RANDOM clone strategy that scrambles which furniture
+        # each env receives; force sequential so env i gets scene i's object.
+        _force_sequential_cloning()
         self._scene = InteractiveScene(scene_cfg)
         if not self.headless:
             self._setup_keyboard()
@@ -649,6 +701,33 @@ def _physics_step(self) -> None:
                 self._sim.render()
             self._scene.update(dt=self._sim.get_physics_dt())
 
+    def flush_contact_cache(self) -> None:
+        """Move every env out of contact and simulate one physics substep there.
+
+        PhysX remembers each link/ground contact from the last simulated
+        substep, and writing a new root pose and joint state does not clear
+        that memory. After evaluation every env is written back to its saved
+        pre-evaluation state; a robot that was still walking when evaluation
+        ended is teleported into its saved mid-stride pose, and the first
+        substep can resolve the old contacts against the new pose and launch
+        it. Teleporting away alone is not enough: PhysX only drops a contact
+        after simulating a substep in which the bodies no longer touch. So every
+        robot goes 50 m underground and its scene objects 20 m further down,
+        one substep runs there, and the caller then overwrites all robot and
+        object state, so these poses never reach training.
+
+        Measured with the real evaluator at 8192 envs: an Agile tracker had 47
+        launches among 177,564 robots still walking when evaluation ended
+        (link speeds up to 4.5e15 m/s, occasionally a non-finite velocity that
+        crashed the run) and 0 of 177,386 with the flush; the G1 bones-deploy
+        tracker had 4 of 57,758 (up to 63 m/s) and 0 of 57,955 with it. The
+        flush costs about 0.1 s per evaluation.
+        """
+        self._move_all_envs_out_of_contact(hide_z=-50.0, object_drop=20.0)
+        self._scene.write_data_to_sim()
+        self._sim.step(render=False)
+        self._scene.update(dt=self._sim.get_physics_dt())
+
     def _apply_simulator_pd_targets(self, pd_targets: torch.Tensor) -> None:
         """Applies PD position targets using IsaacLab's internal PD controller."""
         self._robot.set_joint_position_target(pd_targets.detach(), joint_ids=None)
diff --git a/protomotions/simulator/isaaclab/utils/mjcf_to_usd.py b/protomotions/simulator/isaaclab/utils/mjcf_to_usd.py
index ae3dc105e..c78c76bdb 100644
--- a/protomotions/simulator/isaaclab/utils/mjcf_to_usd.py
+++ b/protomotions/simulator/isaaclab/utils/mjcf_to_usd.py
@@ -18,6 +18,7 @@
 import os
 import xml.etree.ElementTree as ET
 from pathlib import Path
+from tempfile import TemporaryDirectory
 from typing import Any, Callable, Dict, Mapping, MutableMapping, Optional, Tuple, Union
 
 from filelock import FileLock
@@ -30,7 +31,7 @@
 _CONVERSION_CACHE: Dict[Tuple[Any, ...], str] = {}
 
 ConverterFactory = Callable[..., str]
-_MJCF_CONVERTER_CACHE_VERSION = "isaaclab3-d6-workaround-v2"
+_MJCF_CONVERTER_CACHE_VERSION = "isaaclab3-d6-marker-inertia-v3"
 
 
 def _absolute_path(path: Union[os.PathLike, str]) -> str:
@@ -256,7 +257,13 @@ def default_mjcf_converter_factory(**cfg_kwargs: Any) -> str:
 
     install_isaaclab_mjcf_d6_workaround()
 
-    converter = MjcfConverter(MjcfConverterCfg(**cfg_kwargs))
+    from protomotions.simulator.base_simulator.robot_mass_properties import prepare_robot_mjcf
+
+    with TemporaryDirectory(prefix="protomotions-robot-usd-") as directory:
+        prepared_path = prepare_robot_mjcf(cfg_kwargs["asset_path"], directory)
+        converter = MjcfConverter(
+            MjcfConverterCfg(**{**cfg_kwargs, "asset_path": prepared_path})
+        )
     return _absolute_path(converter.usd_path)
 
 
diff --git a/protomotions/simulator/mujoco/config.py b/protomotions/simulator/mujoco/config.py
index 5357ab917..a659615dd 100644
--- a/protomotions/simulator/mujoco/config.py
+++ b/protomotions/simulator/mujoco/config.py
@@ -2,6 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from dataclasses import dataclass, field
+from typing import Optional
 from protomotions.simulator.base_simulator.config import SimParams, SimulatorConfig
 
 
@@ -11,7 +12,8 @@ class MujocoSimParams(SimParams):
 
     Defaults match RoboJuDo: 1000Hz physics, 20x decimation (50Hz control).
     No solver params -- we use MuJoCo's built-in defaults for everything
-    (solver, integrator, iterations, etc.) since RoboJuDo does the same.
+    (solver, iterations, etc.) since RoboJuDo does the same. The integrator
+    can be overridden per robot.
     """
 
     fps: int = field(
@@ -22,6 +24,15 @@ class MujocoSimParams(SimParams):
         default=20,
         metadata={"help": "Number of physics steps per control step."}
     )
+    integrator: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "MuJoCo integrator: 'euler', 'implicitfast', 'implicit' or "
+            "'rk4'. None keeps the MJCF's choice (MuJoCo's default is Euler). "
+            "Euler applies actuator damping explicitly, which can go unstable "
+            "with stiff PD gains on light links; 'implicitfast' does not."
+        },
+    )
 
 
 @dataclass
@@ -39,10 +50,3 @@ class MujocoSimulatorConfig(SimulatorConfig):
             "False: explicit PD torque computation at each physics substep."
         },
     )
-    use_separate_keyboard_window: bool = field(
-        default=True,
-        metadata={
-            "help": "Capture application keys in a separate Tk window instead "
-            "of competing with MuJoCo viewer keyboard shortcuts."
-        },
-    )
diff --git a/protomotions/simulator/mujoco/keyboard.py b/protomotions/simulator/mujoco/keyboard.py
index a9ec08ccb..0c4ddf5d2 100644
--- a/protomotions/simulator/mujoco/keyboard.py
+++ b/protomotions/simulator/mujoco/keyboard.py
@@ -1,160 +1,76 @@
 # SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-"""Separate keyboard capture for the MuJoCo passive viewer.
+"""Task keys in the MuJoCo viewer, without a second window."""
 
-MuJoCo owns keyboard focus for its viewer and reserves many keys for camera
-and UI commands. This module keeps application controls in a small Tk window,
-so the simulator can forward raw transitions without competing with viewer
-callbacks.
-"""
+import logging
+from queue import Empty, SimpleQueue
+from typing import Iterator
 
-from __future__ import annotations
+import numpy as np
 
-import multiprocessing as mp
-import queue
-import string
-from typing import List, Tuple
+log = logging.getLogger(__name__)
 
 
-_CLOSE_EVENT = "__window_closed__"
-_ERROR_EVENT = "__window_error__"
-_KEYS = frozenset(string.ascii_letters + string.digits + string.punctuation + " ")
+class MujocoViewerKeyboard:
+    """Queue viewer-thread presses; dispatch them on the simulation thread.
 
-
-def _printable_key(char: str, keysym: str) -> str:
-    """Return the one-character key represented by a Tk event.
-
-    Tk normally fills ``event.char`` for key presses, but key-release events
-    often expose only ``event.keysym``. Falling back to ``keysym`` prevents a
-    dropped release from latching a key and suppressing later clicks.
+    MuJoCo also runs its own shortcuts before notifying our callback. Task
+    mode restores visualization settings so W/R/L and digits do not leave
+    wireframe, reflections or geom groups toggled. Period switches to native
+    mode for intentional viewer adjustments, without issuing task commands.
     """
-    key = (char or keysym or "").lower()
-    if len(key) != 1 or key not in _KEYS:
-        return ""
-    return key
-
-
-def _run_keyboard_window(events, ready) -> None:
-    """Run the Tk event loop in a process that owns its GUI resources."""
-    try:
-        import tkinter as tk
-
-        root = tk.Tk()
-        root.title("ProtoMotions MuJoCo controls")
-        root.geometry("420x100")
-        root.resizable(False, False)
-        label = tk.Label(
-            root,
-            text=(
-                "Click here, then press your registered ProtoMotions keys.\n"
-                "This window owns task keys; MuJoCo remains visible for viewing."
-            ),
-            padx=12,
-            pady=12,
-        )
-        label.pack(fill=tk.BOTH, expand=True)
-
-        def emit(event, pressed: bool) -> None:
-            key = _printable_key(
-                getattr(event, "char", ""), getattr(event, "keysym", "")
-            )
-            if not key:
-                return
-            try:
-                events.put_nowait((key, pressed))
-            except queue.Full:
-                # A held key can generate OS repeat events. Dropping a
-                # repeated transition is preferable to blocking the GUI.
-                pass
-
-        root.bind_all("<KeyPress>", lambda event: emit(event, True))
-        root.bind_all("<KeyRelease>", lambda event: emit(event, False))
-
-        def close() -> None:
-            try:
-                events.put_nowait((_CLOSE_EVENT, False))
-            except queue.Full:
-                pass
-            root.destroy()
-
-        root.protocol("WM_DELETE_WINDOW", close)
-        # Keep task-key focus within this Tk application without grabbing the
-        # entire desktop. A global grab would also intercept window-manager
-        # shortcuts and make the user's session appear frozen.
-        root.deiconify()
-        root.update_idletasks()
-        root.update()
-        root.grab_set()
-        root.focus_force()
-        ready.set()
-        root.mainloop()
-    except BaseException as exc:
-        try:
-            events.put_nowait((_ERROR_EVENT, repr(exc)))
-        except queue.Full:
-            pass
-        ready.set()
-
-
-class MujocoKeyboardWindow:
-    """Own a separate process that captures printable key transitions."""
 
     def __init__(self) -> None:
-        context = mp.get_context("spawn")
-        self._events = context.Queue(maxsize=256)
-        self._ready = context.Event()
-        self._process = context.Process(
-            target=_run_keyboard_window,
-            args=(self._events, self._ready),
-            daemon=True,
-        )
-        self._process.start()
-        if not self._ready.wait(timeout=5.0):
-            self.close()
-            raise RuntimeError("MuJoCo keyboard window did not start")
-        if not self._process.is_alive():
-            error = self._read_startup_error()
-            self.close()
-            raise RuntimeError(f"MuJoCo keyboard window failed to start: {error}")
+        self._events = SimpleQueue()
+        self.native = False
+        self._baseline = None
 
-    def _read_startup_error(self) -> str:
-        try:
-            event = self._events.get_nowait()
-        except queue.Empty:
-            return "keyboard window process exited"
-        if event[0] == _ERROR_EVENT:
-            return event[1]
-        return repr(event)
+    def on_key(self, keycode: int) -> None:
+        # The passive viewer supplies discrete presses, not key releases.
+        # Never mutate physics or invoke application callbacks on its thread.
+        if 33 <= keycode <= 126:
+            self._events.put(chr(keycode))
 
-    def drain_events(self) -> List[Tuple[str, bool]]:
-        """Return all queued ``(key, pressed)`` transitions."""
-        events: List[Tuple[str, bool]] = []
+    def drain_presses(self) -> Iterator[str]:
         while True:
             try:
-                event = self._events.get_nowait()
-            except queue.Empty:
-                break
+                yield self._events.get_nowait()
+            except Empty:
+                return
 
-            key, value = event
-            if key == _ERROR_EVENT:
-                raise RuntimeError(f"MuJoCo keyboard window failed: {value}")
-            if key == _CLOSE_EVENT:
-                raise RuntimeError("MuJoCo keyboard window was closed")
-            events.append((key, bool(value)))
+    def toggle_native(self) -> None:
+        self.native = not self.native
+        log.info(
+            "MuJoCo keyboard: %s",
+            "native viewer shortcuts" if self.native else "task controls",
+        )
 
-        if not self._process.is_alive():
-            raise RuntimeError("MuJoCo keyboard window exited unexpectedly")
-        return events
+    def capture(self, viewer) -> None:
+        with viewer.lock():
+            self._baseline = (
+                viewer.opt.flags.copy(),
+                viewer.opt.geomgroup.copy(),
+                viewer.user_scn.flags.copy(),
+            )
 
-    def close(self) -> None:
-        """Stop the child process and release its IPC resources."""
-        process = getattr(self, "_process", None)
-        if process is not None:
-            if process.is_alive():
-                process.terminate()
-            process.join(timeout=1.0)
-        events = getattr(self, "_events", None)
-        if events is not None:
-            events.close()
-            events.join_thread()
+    def sync(self, viewer) -> None:
+        if self.native:
+            viewer.sync()
+            return
+        if self._baseline is None:
+            self.capture(viewer)
+        flags, groups, render_flags = self._baseline
+        with viewer.lock():
+            viewer.opt.flags[:] = flags
+            viewer.opt.geomgroup[:] = groups
+        viewer.sync()
+        # sync first copies native render flags into user_scn. Only a *change*
+        # to user_scn is propagated back, so setting it before that first sync
+        # cannot undo a native shortcut. A second sync is needed only on change.
+        with viewer.lock():
+            changed = not np.array_equal(viewer.user_scn.flags, render_flags)
+            if changed:
+                viewer.user_scn.flags[:] = render_flags
+        if changed:
+            viewer.sync()
diff --git a/protomotions/simulator/mujoco/recording.py b/protomotions/simulator/mujoco/recording.py
new file mode 100644
index 000000000..01ff283eb
--- /dev/null
+++ b/protomotions/simulator/mujoco/recording.py
@@ -0,0 +1,81 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Offscreen capture of MuJoCo's live camera and debug geometry.
+
+Callers hold the passive viewer lock while reading its state and rendering.
+Clip lifetime and video encoding remain in the shared recording mixin.
+"""
+
+from __future__ import annotations
+
+import mujoco
+import numpy as np
+
+
+def recording_dimensions(viewer) -> tuple[int, int]:
+    """Return even viewport dimensions (width, height), or a 640×480 fallback."""
+    viewport = getattr(viewer, "viewport", None)
+    width, height = 640, 480
+    if viewport is not None and min(viewport.width, viewport.height) > 0:
+        width, height = viewport.width, viewport.height
+    # H.264's usual yuv420p pixel format requires even dimensions.
+    return max(2, int(width) // 2 * 2), max(2, int(height) // 2 * 2)
+
+
+def render_viewport(
+    model: mujoco.MjModel,
+    data: mujoco.MjData,
+    viewer,
+    width: int,
+    height: int,
+) -> np.ndarray:
+    """Render RGB pixels with the live camera, options, flags, and markers."""
+    model.vis.global_.offwidth = max(model.vis.global_.offwidth, width)
+    model.vis.global_.offheight = max(model.vis.global_.offheight, height)
+    # update_scene rebuilds model geometry only; user_scn holds goal markers
+    # and arrows that must be appended to the offscreen scene separately.
+    debug_scene = viewer.user_scn if viewer is not None else None
+    renderer = mujoco.Renderer(
+        model,
+        height=height,
+        width=width,
+        max_geom=max(10000, model.ngeom)
+        + (debug_scene.maxgeom if debug_scene is not None else 0),
+    )
+    try:
+        if viewer is not None:
+            renderer.update_scene(data, camera=viewer.cam, scene_option=viewer.opt)
+            scene = renderer.scene
+            scene.flags[:] = debug_scene.flags
+            for i in range(debug_scene.ngeom):
+                source = debug_scene.geoms[i]
+                dest = scene.geoms[scene.ngeom]
+                for field in ("size", "pos", "mat", "rgba"):
+                    getattr(dest, field)[:] = getattr(source, field)
+                # Preserve material, label and segmentation metadata too.
+                for field in (
+                    "type",
+                    "dataid",
+                    "objtype",
+                    "objid",
+                    "category",
+                    "matid",
+                    "texcoord",
+                    "segid",
+                    "emission",
+                    "specular",
+                    "shininess",
+                    "reflectance",
+                    "camdist",
+                    "modelrbound",
+                    "transparent",
+                    "label",
+                ):
+                    setattr(dest, field, getattr(source, field))
+                scene.ngeom += 1
+        else:
+            renderer.update_scene(data)
+        return renderer.render()
+    finally:
+        renderer.close()
diff --git a/protomotions/simulator/mujoco/simulator.py b/protomotions/simulator/mujoco/simulator.py
index 908001604..ec9e420c1 100644
--- a/protomotions/simulator/mujoco/simulator.py
+++ b/protomotions/simulator/mujoco/simulator.py
@@ -17,7 +17,6 @@
 
 log = logging.getLogger(__name__)
 
-from protomotions.assets import resolve_asset_root
 from protomotions.simulator.base_simulator.config import (
     MarkerState,
     ProjectileConfig,
@@ -33,7 +32,17 @@
     StateConversion,
 )
 from protomotions.simulator.mujoco.config import MujocoSimulatorConfig
-from protomotions.simulator.mujoco.keyboard import MujocoKeyboardWindow
+from protomotions.simulator.mujoco.keyboard import MujocoViewerKeyboard
+from protomotions.simulator.mujoco.recording import recording_dimensions, render_viewport
+
+
+# Names accepted by MujocoSimParams.integrator
+_INTEGRATORS = {
+    "euler": mujoco.mjtIntegrator.mjINT_EULER,
+    "rk4": mujoco.mjtIntegrator.mjINT_RK4,
+    "implicit": mujoco.mjtIntegrator.mjINT_IMPLICIT,
+    "implicitfast": mujoco.mjtIntegrator.mjINT_IMPLICITFAST,
+}
 
 
 def _to_torch_f32(arr: np.ndarray) -> torch.Tensor:
@@ -41,6 +50,28 @@ def _to_torch_f32(arr: np.ndarray) -> torch.Tensor:
     return torch.from_numpy(arr.astype(np.float32))
 
 
+# A free joint's qvel[3:6] is angular velocity in the BODY frame, while its qvel[0:3]
+# is linear velocity in the world frame. ProtoMotions' root_ang_vel is world-frame, so
+# every read and write of qvel[3:6] goes through these two helpers.
+def _world_to_body(quat_wxyz: np.ndarray, vec: np.ndarray) -> np.ndarray:
+    """Express a world-frame vector in the frame of a body oriented by quat_wxyz."""
+    quat = np.asarray(quat_wxyz, dtype=np.float64)
+    inverse = quat * np.array([1.0, -1.0, -1.0, -1.0]) / np.linalg.norm(quat)
+    out = np.zeros(3)
+    mujoco.mju_rotVecQuat(out, np.asarray(vec, dtype=np.float64), inverse)
+    return out
+
+
+def _body_to_world(quat_wxyz: np.ndarray, vec: np.ndarray) -> np.ndarray:
+    """Express a body-frame vector in the world frame, for a body oriented by quat_wxyz."""
+    quat = np.asarray(quat_wxyz, dtype=np.float64)
+    out = np.zeros(3)
+    mujoco.mju_rotVecQuat(
+        out, np.asarray(vec, dtype=np.float64), quat / np.linalg.norm(quat)
+    )
+    return out
+
+
 class MujocoSimulator(Simulator):
     """MuJoCo CPU-only simulator backend.
 
@@ -88,7 +119,7 @@ def __init__(
         self.data: Optional[mujoco.MjData] = None
         self.viewer = None
         self._viewer_initialized = False
-        self._keyboard_window = None
+        self._viewer_keyboard = MujocoViewerKeyboard()
 
         # Cached control parameters
         self._kp = None  # [num_dofs] stiffness in common DOF order
@@ -292,9 +323,7 @@ def _add_ground_and_light(root: ET.Element) -> None:
     def _create_simulation(self) -> None:
         """Create the MuJoCo simulation environment."""
         # Load MJCF model
-        asset_root = resolve_asset_root(self.robot_config.asset.asset_root)
-        asset_file = self.robot_config.asset.asset_file_name
-        asset_path = os.path.join(asset_root, asset_file)
+        asset_path = self._get_robot_asset_path()
 
         # MJCF injection below needs _proj_config before _init_projectiles runs
         self._resolve_proj_config()
@@ -310,12 +339,28 @@ def _create_simulation(self) -> None:
             f"MuJoCo timestep: {self.model.opt.timestep:.4f}s ({self.config.sim.fps}Hz)"
         )
 
+        # Integrator: None keeps whatever the MJCF's <option> chose. getattr
+        # because sim params pickled before this field existed lack it.
+        integrator = getattr(self.config.sim, "integrator", None)
+        if integrator is not None:
+            if integrator not in _INTEGRATORS:
+                raise ValueError(
+                    f"Unknown MuJoCo integrator '{integrator}'; "
+                    f"expected one of {sorted(_INTEGRATORS)}"
+                )
+            self.model.opt.integrator = _INTEGRATORS[integrator]
+        integrator_name = mujoco.mjtIntegrator(self.model.opt.integrator).name
+        print(f"MuJoCo integrator: {integrator_name}")
+
         # Zero passive forces from MJCF (we handle PD control via actuators)
         self._zero_passive_forces()
 
         # Override armature and frictionloss from robot config
         self._override_joint_properties()
 
+        # ctrl must be the joint torque in every control mode
+        self._neutralize_actuator_gear()
+
         # Build actuator-to-DOF mapping
         self._build_actuator_mapping()
 
@@ -432,6 +477,62 @@ def _override_joint_properties(self) -> None:
                     f"frictionloss -> 0.0"
                 )
 
+    def _neutralize_actuator_gear(self) -> None:
+        """Set every joint actuator's gear to 1 so that ctrl is the joint torque.
+
+        MJCF motors may carry a gear ratio (SMPL and SMPL-X use 500, SOMA23 and
+        AMP 50-250), and MuJoCo applies gear * actuator force to the joint.
+        ProtoMotions writes joint torques straight into ctrl, and the position
+        actuators built in _configure_actuators_for_pd compute the PD torque
+        themselves, so a non-unit gear multiplies every torque and its effort
+        limit by the gear, and also pulls a position actuator toward
+        target / gear instead of target. Any ctrl or force range the MJCF
+        declares is rescaled so it keeps the same meaning in joint units.
+
+        Only a positive scalar gear on a hinge or slide joint can be reset this
+        way, so anything else is rejected rather than guessed at.
+        """
+        is_joint = self.model.actuator_trntype == mujoco.mjtTrn.mjTRN_JOINT
+        for act_idx in np.flatnonzero(is_joint):
+            self._check_resettable_gear(act_idx)
+
+        gear = self.model.actuator_gear[:, 0].copy()
+        to_fix = np.flatnonzero(is_joint & (gear != 1.0))
+        if to_fix.size == 0:
+            return
+
+        for act_idx in to_fix:
+            g = gear[act_idx]
+            self.model.actuator_ctrlrange[act_idx] *= g
+            self.model.actuator_forcerange[act_idx] *= g
+        self.model.actuator_gear[to_fix] = 0.0
+        self.model.actuator_gear[to_fix, 0] = 1.0
+        print(
+            f"  Reset actuator gear to 1 on {to_fix.size} actuators "
+            f"(MJCF gears {sorted(set(gear[to_fix].tolist()))})"
+        )
+
+    def _check_resettable_gear(self, act_idx: int) -> None:
+        """Reject a joint actuator whose gear cannot be reset to a scalar 1."""
+        name = mujoco.mj_id2name(self.model, mujoco.mjtObj.mjOBJ_ACTUATOR, act_idx)
+        # A Python int, not numpy's: from mujoco 3.16 on, `member == numpy_int`
+        # is False for an mjtJoint member, and that is the order `in` compares.
+        joint_type = int(self.model.jnt_type[self.model.actuator_trnid[act_idx, 0]])
+        if joint_type not in (mujoco.mjtJoint.mjJNT_HINGE, mujoco.mjtJoint.mjJNT_SLIDE):
+            raise ValueError(
+                f"MuJoCo actuator '{name}' drives a "
+                f"{mujoco.mjtJoint(joint_type).name} joint. This backend supports "
+                "actuators on hinge and slide joints only: a ball joint's gear is a "
+                "3D torque axis, not a scalar ratio, so it cannot be reset to 1."
+            )
+        gear = self.model.actuator_gear[act_idx, 0]
+        if gear <= 0.0:
+            raise ValueError(
+                f"MuJoCo actuator '{name}' has gear {gear:g}. This backend writes "
+                "joint torques into ctrl and needs a positive gear: a zero gear "
+                "disconnects the actuator, and a negative one flips the torque's sign."
+            )
+
     def _build_actuator_mapping(self) -> None:
         """Build mapping from sim DOF index to MuJoCo actuator index.
 
@@ -501,7 +602,8 @@ def _configure_actuators_for_pd(self) -> None:
             biastype = mjBIAS_AFFINE (1)
 
         After this, data.ctrl[i] = target_position for actuator i.
-        MuJoCo handles the PD at every substep internally.
+        MuJoCo handles the PD at every substep internally. This relies on
+        _neutralize_actuator_gear having set the gear to 1.
         """
         from protomotions.robot_configs.base import ControlType
 
@@ -616,15 +718,14 @@ def _init_viewer(self) -> None:
             self.data,
             show_left_ui=False,
             show_right_ui=False,
-            key_callback=(
-                None
-                if self.config.use_separate_keyboard_window
-                else self._mujoco_key_callback
-            ),
+            key_callback=self._viewer_keyboard.on_key,
         )
         self._viewer_initialized = True
-        if self.config.use_separate_keyboard_window:
-            self._keyboard_window = MujocoKeyboardWindow()
+        self._viewer_keyboard.capture(self.viewer)
+        self.user_interface.register_key(
+            ".", owner="mujoco", description="Toggle task / native viewer keys",
+            on_press=self._viewer_keyboard.toggle_native,
+        )
         # Ensure viewer is closed on exit to prevent hangs
         atexit.register(self._close_viewer)
 
@@ -636,9 +737,8 @@ def _init_viewer(self) -> None:
         self.viewer.cam.type = mujoco.mjtCamera.mjCAMERA_TRACKING
 
         log.info(
-            "MuJoCo passive viewer launched (tracking body 1); "
-            "separate keyboard window=%s",
-            self.config.use_separate_keyboard_window,
+            "MuJoCo viewer: click the viewer to use task keys. "
+            "Press '.' to toggle native viewer shortcuts."
         )
 
     @staticmethod
@@ -710,9 +810,6 @@ def _add_mujoco_marker_geom(
 
     def _close_viewer(self) -> None:
         """Close the viewer if it's still running."""
-        if self._keyboard_window is not None:
-            self._keyboard_window.close()
-            self._keyboard_window = None
         if self.viewer is not None and self._viewer_initialized:
             try:
                 self.viewer.close()
@@ -774,7 +871,7 @@ def _set_simulator_env_state(
             self.data.qpos[7 : 7 + self._num_actuated_dofs] = dof_pos
 
             self.data.qvel[0:3] = root_vel
-            self.data.qvel[3:6] = root_ang_vel
+            self.data.qvel[3:6] = _world_to_body(root_rot, root_ang_vel)
             self.data.qvel[6 : 6 + self._num_actuated_dofs] = dof_vel
         else:
             self.data.qpos[: self._num_actuated_dofs] = dof_pos
@@ -843,10 +940,9 @@ def _physics_step(self) -> None:
 
     def _poll_keyboard_events(self) -> None:
         """Forward queued application keys before the next physics step."""
-        if self._keyboard_window is None:
-            return
-        for key, pressed in self._keyboard_window.drain_events():
-            self.user_interface.handle_key_event(key, pressed=pressed)
+        for key in self._viewer_keyboard.drain_presses():
+            if key == "." or not self._viewer_keyboard.native:
+                self.user_interface.handle_key_press(key)
 
     def _print_state_debug(self) -> None:
         """Print state summary for debugging."""
@@ -892,7 +988,7 @@ def _apply_root_velocity_impulse(
         ang_vel = angular_velocity[0].cpu().numpy()
 
         self.data.qvel[0:3] += lin_vel
-        self.data.qvel[3:6] += ang_vel
+        self.data.qvel[3:6] += _world_to_body(self.data.qpos[3:7], ang_vel)
 
     def _get_simulator_root_state(
         self, env_ids: Optional[torch.Tensor] = None
@@ -902,7 +998,7 @@ def _get_simulator_root_state(
             root_pos = self.data.qpos[0:3].copy()
             root_rot = self.data.qpos[3:7].copy()
             root_vel = self.data.qvel[0:3].copy()
-            root_ang_vel = self.data.qvel[3:6].copy()
+            root_ang_vel = _body_to_world(root_rot, self.data.qvel[3:6])
         else:
             root_pos = np.zeros(3, dtype=np.float32)
             root_rot = np.array([1.0, 0.0, 0.0, 0.0], dtype=np.float32)
@@ -925,9 +1021,20 @@ def _get_simulator_bodies_state(
         body_pos = self.data.xpos[1 : 1 + nb, :].copy()
         body_rot = self.data.xquat[1 : 1 + nb, :].copy()
 
-        # cvel is [ang_vel(3), lin_vel(3)]
-        body_ang_vel = self.data.cvel[1 : 1 + nb, 0:3].copy()
-        body_vel = self.data.cvel[1 : 1 + nb, 3:6].copy()
+        # cvel is expressed about the kinematic tree's COM, not each body.
+        # XBODY gives velocity at xpos (not the inertial COM), in world axes.
+        body_velocity = np.empty((nb, 6), dtype=np.float64)
+        for index in range(nb):
+            mujoco.mj_objectVelocity(
+                self.model,
+                self.data,
+                mujoco.mjtObj.mjOBJ_XBODY,
+                index + 1,
+                body_velocity[index],
+                0,
+            )
+        body_ang_vel = body_velocity[:, :3]
+        body_vel = body_velocity[:, 3:]
 
         return RobotState(
             rigid_body_pos=_to_torch_f32(body_pos).unsqueeze(0),
@@ -1136,7 +1243,9 @@ def _set_projectile_root_states(
             self.data.qpos[qp : qp + 3] = positions[i].cpu().numpy()
             self.data.qpos[qp + 3 : qp + 7] = rot_wxyz[i].cpu().numpy()
             self.data.qvel[qv : qv + 3] = velocities[i].cpu().numpy()
-            self.data.qvel[qv + 3 : qv + 6] = ang_velocities[i].cpu().numpy()
+            self.data.qvel[qv + 3 : qv + 6] = _world_to_body(
+                rot_wxyz[i].cpu().numpy(), ang_velocities[i].cpu().numpy()
+            )
 
             # Toggle collisions based on whether this is a hide or throw
             z_pos = positions[i, 2].item()
@@ -1150,28 +1259,8 @@ def _set_projectile_root_states(
 
         mujoco.mj_forward(self.model, self.data)
 
-    def _mujoco_key_callback(self, keycode: int) -> None:
-        """Handle ASCII keyboard events from MuJoCo passive viewer.
-
-        MuJoCo special keys use non-ASCII key codes that do not correspond to
-        the string keys registered in ``UserInterface``. Ignore those codes so a
-        special key cannot be normalized into an unrelated Unicode character.
-        """
-        if keycode < 0 or keycode > 127:
-            return
-        # MuJoCo's callback is a discrete press notification; it does not
-        # deliver a matching release. Record it as an edge so repeated
-        # presses remain independent instead of leaving a key latched.
-        self.user_interface.handle_key_press(chr(keycode))
-
     def _write_viewport_to_file(self, file_name: str) -> None:
-        """Render the live viewer camera to a file.
-
-        ``Renderer.update_scene`` defaults to MuJoCo's free camera when no
-        camera is supplied. Reuse the passive viewer camera so recordings
-        match the view shown on screen.
-        """
-        renderer = mujoco.Renderer(self.model, height=480, width=640)
+        """Save a frame using fixed clip dimensions and the live viewer state."""
         viewer = self.viewer if self._viewer_initialized else None
         lock = (
             viewer.lock()
@@ -1179,16 +1268,15 @@ def _write_viewport_to_file(self, file_name: str) -> None:
             else nullcontext()
         )
         with lock:
-            if viewer is not None:
-                renderer.update_scene(self.data, camera=viewer.cam)
-            else:
-                renderer.update_scene(self.data)
-            pixels = renderer.render()
+            # Encoders require a fixed frame size, even if the window resizes.
+            if self._user_recording_frame == 0:
+                self._recording_viewport_size = recording_dimensions(viewer)
+            width, height = self._recording_viewport_size
+            pixels = render_viewport(self.model, self.data, viewer, width, height)
 
         import matplotlib.pyplot as plt
 
         plt.imsave(file_name, pixels)
-        renderer.close()
 
     def _init_camera(self) -> None:
         """Initialize camera position and orientation."""
@@ -1256,6 +1344,6 @@ def render(self) -> None:
             # interactions that switch the camera mode don't lose the robot.
             self.viewer.cam.type = mujoco.mjtCamera.mjCAMERA_TRACKING
             self.viewer.cam.trackbodyid = 1
-            self.viewer.sync()
+            self._viewer_keyboard.sync(self.viewer)
 
         super().render()
diff --git a/protomotions/simulator/newton/simulator.py b/protomotions/simulator/newton/simulator.py
index 1a9abd096..63cb5ef97 100644
--- a/protomotions/simulator/newton/simulator.py
+++ b/protomotions/simulator/newton/simulator.py
@@ -1,12 +1,10 @@
 # SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-import os
 import torch
 import numpy as np
 from typing import Dict, List, Optional, Tuple
 
-from protomotions.assets import resolve_asset_root
 from protomotions.simulator.base_simulator.simulator import Simulator
 from protomotions.simulator.base_simulator.config import (
     MarkerState,
@@ -204,9 +202,7 @@ def _create_envs(self) -> None:
         Follows the Newton G1 example pattern: configure joint properties
         on the builder BEFORE finalize, then use replicate() for multi-env.
         """
-        asset_root = resolve_asset_root(self.robot_config.asset.asset_root)
-        asset_file = self.robot_config.asset.asset_file_name
-        asset_path = os.path.join(asset_root, asset_file)
+        asset_path = self._get_robot_asset_path()
 
         print(f"Loading robot from: {asset_path}")
 
@@ -861,24 +857,31 @@ def _physics_step(self) -> None:
         self.sim_time += self.frame_dt
 
     def _apply_newton_control(self, actions: torch.Tensor) -> None:
-        """Update Newton's explicit control buffers for one action."""
+        """Update Newton's explicit control buffers for one action.
+
+        Mirrors the base ``_apply_control``: actions arrive already processed
+        (PD targets for PROPORTIONAL, torques for TORQUE), so they are used
+        as-is. The PD law itself runs in a Warp kernel inside ``_simulate``.
+        """
         if self.control_type == ControlType.PROPORTIONAL:
-            pd_tar = self._action_to_pd_targets(actions)
+            pd_tar = actions
             if (
                 self._domain_randomization is not None
                 and "action_noise" in self._domain_randomization
             ):
+                pd_tar = pd_tar.clone()
                 pd_tar[
                     ..., self._domain_randomization["action_noise"]["dof_indices"]
                 ] += self._domain_randomization["action_noise"]["action_noise"]
             sim_targets = pd_tar[:, self.data_conversion.dof_convert_to_sim]
             self._update_pd_targets(sim_targets)
         elif self.control_type == ControlType.TORQUE:
-            torques = self._action_to_torque_targets(actions)
+            torques = actions
             if (
                 self._domain_randomization is not None
                 and "action_noise" in self._domain_randomization
             ):
+                torques = torques.clone()
                 torques[
                     ..., self._domain_randomization["action_noise"]["dof_indices"]
                 ] += self._domain_randomization["action_noise"]["action_noise"]
@@ -1009,28 +1012,6 @@ def _get_simulator_bodies_contact_binary(
 
         return contact_binary
 
-    def park_envs(
-        self,
-        env_ids: torch.Tensor,
-        hide_z: float = -50.0,
-    ) -> None:
-        """No-op on Newton: evaluation env-parking is unnecessary and harmful.
-
-        The base implementation teleports inactive envs to ``hide_z`` to keep
-        their AABBs out of PhysX's broadphase pair budget. Newton replicates
-        environments as separate worlds with fixed per-world contact buffers,
-        so parking buys nothing here.
-
-        Worse, a parked robot free-falls with PD control still driving its
-        joints toward policy targets: energy is pumped in with no contact to
-        dissipate it, joint velocities diverge, and the resulting non-finite
-        values persist in solver warm-start memory — permanently poisoning the
-        parked envs even after their kinematic state is restored. Leaving
-        inactive envs simulating in place (exactly as they do during training)
-        is both safe and cheap.
-        """
-        return None
-
     def _get_simulator_bodies_state(
         self, env_ids: Optional[torch.Tensor] = None
     ) -> RobotState:
diff --git a/protomotions/tests/test_action_functions.py b/protomotions/tests/test_action_functions.py
index 83df3160b..0a9b90521 100644
--- a/protomotions/tests/test_action_functions.py
+++ b/protomotions/tests/test_action_functions.py
@@ -191,7 +191,7 @@ def test_make_pd_action_config_builds_normalized_action_config_from_robot_data()
     assert torch.equal(config["damping"], torch.tensor([1.0, 2.0, 4.0, 5.0, 10.0]))
 
 
-def test_make_bm_pd_action_config_uses_default_pose_and_effort_over_stiffness_scale():
+def test_make_bm_pd_action_config_uses_default_pose_and_quarter_effort_scale():
     robot_config = _mixed_dof_robot_config()
 
     config = make_bm_pd_action_config(robot_config)
@@ -200,7 +200,7 @@ def test_make_bm_pd_action_config_uses_default_pose_and_effort_over_stiffness_sc
     assert torch.equal(config["pd_action_offset"], robot_config.default_dof_pos)
     _assert_no_storage_alias(config["pd_action_offset"], robot_config.default_dof_pos)
     assert torch.allclose(
-        config["action_scale"], torch.tensor([10.0, 4.0, 1.5, 0.5, 0.1])
+        config["action_scale"], torch.tensor([2.5, 1.0, 0.375, 0.125, 0.025])
     )
     assert torch.equal(
         config["stiffness"], torch.tensor([10.0, 20.0, 40.0, 50.0, 100.0])
@@ -208,6 +208,37 @@ def test_make_bm_pd_action_config_uses_default_pose_and_effort_over_stiffness_sc
     assert torch.equal(config["damping"], torch.tensor([1.0, 2.0, 4.0, 5.0, 10.0]))
 
 
+def test_bm_unit_action_produces_quarter_effort_at_default_pose():
+    robot = _mixed_dof_robot_config()
+    config = make_bm_pd_action_config(robot)
+    fn = config.pop("fn")
+    result = fn(torch.ones(2, 5), **config)
+    torque = config["stiffness"] * (result["processed_action"] - robot.default_dof_pos)
+    expected = 0.25 * torch.tensor([100.0, 80.0, 60.0, 25.0, 10.0])
+    torch.testing.assert_close(torque, expected.expand_as(torque))
+
+
+def test_bm_recipe_penalizes_raw_action_rate_with_unchanged_weight():
+    from argparse import Namespace
+
+    from examples.experiments.mimic.mlp_bm_l2c2 import env_config
+    from protomotions.envs.rewards import compute_action_rate_l2
+
+    cfg = env_config(_mixed_dof_robot_config(), Namespace())
+    reward = cfg.reward_components["action_rate"]
+    assert reward.compute_func is compute_action_rate_l2
+    assert {key: str(path) for key, path in reward.dynamic_vars.items()} == {
+        "raw_action": "current_action",
+        "previous_raw_action": "previous_action",
+    }
+    assert reward.static_params["weight"] == -0.1
+    assert cfg.num_state_history_steps >= 1
+    torch.testing.assert_close(
+        cfg.action_config["action_scale"],
+        torch.tensor([2.5, 1.0, 0.375, 0.125, 0.025]),
+    )
+
+
 def test_make_passthrough_pd_action_config_builds_fixed_gain_config_only():
     robot_config = _mixed_dof_robot_config()
 
diff --git a/protomotions/tests/test_base_agent_fit_helpers.py b/protomotions/tests/test_base_agent_fit_helpers.py
index 231074771..0ef2cee55 100644
--- a/protomotions/tests/test_base_agent_fit_helpers.py
+++ b/protomotions/tests/test_base_agent_fit_helpers.py
@@ -3,6 +3,7 @@
 
 """Lightweight lifecycle tests for BaseAgent.fit without simulator startup."""
 
+from pathlib import Path
 from types import SimpleNamespace
 
 import torch
@@ -169,6 +170,9 @@ def _fit_agent(calls, buffer, *, max_epochs=1):
     agent.best_evaluated_score = None
     agent._should_stop = False
     agent.fabric = _Fabric()
+    # A dir with no last.ckpt, so the loop-entry "reached training" save fires (it is
+    # written only when absent). Overridden by tests that check the resume/skip path.
+    agent.root_dir = Path("nonexistent_fit_test_dir")
     agent.env = _FitEnv()
     agent.motion_shard_cycle = 0
     agent.motion_lib = SimpleNamespace(
@@ -368,3 +372,44 @@ def test_checkpoint_evaluation_does_not_advance_a_scheduled_motion_shard(
 
     assert ("evaluate",) in calls
     assert ("unexpected_shard_advance",) not in calls
+
+
+def test_fit_writes_initial_checkpoint_only_when_last_ckpt_absent(monkeypatch, tmp_path):
+    # The loop-entry save (a resume point + the "reached training" signal the cluster
+    # retry loop reads) fires ONLY when last.ckpt is absent. On a resume the loaded
+    # checkpoint already exists, and re-writing it every attempt would advance the
+    # mtime with no real progress -- which continue-mode run_with_retry would misread
+    # as training progress, masking a deterministic first-epoch crash.
+    def _run(root_dir):
+        calls = []
+        monkeypatch.setattr(base_agent_module, "ExperienceBuffer", _FitExperienceBuffer)
+        monkeypatch.setattr(
+            base_agent_module, "track", lambda it, description=None: it
+        )
+        monkeypatch.setattr(
+            base_agent_module,
+            "aggregate_scalar_metrics",
+            lambda metrics, fabric, weight=1: dict(metrics),
+        )
+        agent = _fit_agent(calls, buffer=None)
+        agent.root_dir = root_dir
+        BaseAgent.fit(agent)
+        return calls
+
+    def _first(calls, item):
+        return next((i for i, c in enumerate(calls) if c == item), len(calls))
+
+    save_last = ("save", "last.ckpt", False)
+    optimize = ("optimize",)
+
+    # Absent -> the loop-entry save happens BEFORE the first epoch's optimize.
+    fresh = _run(tmp_path / "fresh")
+    assert _first(fresh, save_last) < _first(fresh, optimize)
+
+    # Present (a resume) -> the loop-entry save is skipped; the only last.ckpt save is
+    # the periodic one, which comes AFTER the first optimize.
+    resumed_dir = tmp_path / "resumed"
+    resumed_dir.mkdir()
+    (resumed_dir / "last.ckpt").write_bytes(b"x")
+    resumed = _run(resumed_dir)
+    assert _first(resumed, save_last) > _first(resumed, optimize)
diff --git a/protomotions/tests/test_base_env_helpers.py b/protomotions/tests/test_base_env_helpers.py
index c23c02dcf..4d6fa7a1f 100644
--- a/protomotions/tests/test_base_env_helpers.py
+++ b/protomotions/tests/test_base_env_helpers.py
@@ -840,6 +840,7 @@ def test_base_env_cached_body_ids_and_clean_context_without_history():
     assert ctx.noisy_historical is None
     assert ctx.previous_action is None
     assert ctx.previous_processed_action is None
+    assert ctx.current_action is env._current_raw_action
     assert ctx.current_processed_action is env._current_processed_action
     assert BaseEnv.context.fget(env) is ctx
     assert env.control_manager.populate_calls[-1] is ctx
@@ -1413,6 +1414,55 @@ def __init__(self, **kwargs):
     assert env.simulator.closed is True
 
 
+class _ContactCachingSimulator(_FakeSimulator):
+    """Fake backend that remembers contacts and records the call order."""
+
+    def __init__(self, events, **kwargs):
+        super().__init__(**kwargs)
+        self.events = events
+
+    def flush_contact_cache(self):
+        self.events.append("flush_contact_cache")
+
+    def reset_envs(self, new_states, new_object_states=None, env_ids=None):
+        self.events.append("reset_envs")
+        super().reset_envs(new_states, new_object_states, env_ids)
+
+
+def test_restore_state_flushes_contacts_before_writing_state_back():
+    # PhysX keeps each robot's contacts from the last substep, and a state write
+    # does not clear them. If the flush ran after the write-back (or not at all),
+    # robots restored into a grounded pose could be launched on the next substep.
+    env = _make_env(scenes=1)
+    events = []
+    env.simulator = _ContactCachingSimulator(events, num_envs=env.num_envs)
+    env.simulator.config._target_ = "fake.IsaacLab"
+    snapshot = BaseEnv.save_state(env)
+
+    BaseEnv.restore_state(env, snapshot)
+
+    assert events == ["flush_contact_cache", "reset_envs"]
+    new_states, new_object_states, env_ids = env.simulator.reset_calls[-1]
+    assert new_states is snapshot["robot_state"]
+    assert new_object_states is snapshot["object_state"]
+    assert torch.equal(env_ids, torch.arange(env.num_envs))
+
+
+def test_restore_state_works_with_a_simulator_that_cannot_flush():
+    env = _make_env()
+    assert not hasattr(env.simulator, "flush_contact_cache")
+    env.simulator.config._target_ = "fake.IsaacLab"
+    env.progress_buf[:] = torch.tensor([4, 5, 6])
+    snapshot = BaseEnv.save_state(env)
+    env.progress_buf.zero_()
+
+    BaseEnv.restore_state(env, snapshot)
+
+    assert len(env.simulator.reset_calls) == 1
+    assert env.simulator.reset_calls[0][0] is snapshot["robot_state"]
+    assert torch.equal(env.progress_buf, torch.tensor([4, 5, 6]))
+
+
 def test_base_env_constructor_initializes_lightweight_dependencies(monkeypatch):
     class _ConstructedMotionManager:
         def __init__(self, **kwargs):
diff --git a/protomotions/tests/test_base_simulator_lifecycle.py b/protomotions/tests/test_base_simulator_lifecycle.py
index 3069ba94a..8272a9530 100644
--- a/protomotions/tests/test_base_simulator_lifecycle.py
+++ b/protomotions/tests/test_base_simulator_lifecycle.py
@@ -379,7 +379,7 @@ def test_simulator_step_applies_control_markers_push_and_projectile_timers():
     assert sim._proj_sim_time[1] == 0.0
 
 
-def test_simulator_reset_and_park_env_paths_with_and_without_objects():
+def test_simulator_reset_paths_with_and_without_objects():
     sim = _sim(scene_objects=1)
     sim._initialize_with_markers(None)
     env_ids = torch.tensor([0])
@@ -407,12 +407,6 @@ def test_simulator_reset_and_park_env_paths_with_and_without_objects():
     assert torch.equal(sim._common_actions[env_ids], torch.zeros(1, 2))
     assert torch.equal(sim._previous_actions[env_ids], torch.zeros(1, 2))
 
-    sim.park_envs(torch.tensor([], dtype=torch.long))
-    sim.park_envs(torch.tensor([0]))
-    parked_state, parked_objects, _ = sim.reset_calls[-1]
-    assert torch.equal(parked_state.root_pos[:, 2], torch.tensor([-50.0]))
-    assert torch.equal(parked_objects.root_pos[..., 2], torch.tensor([[-51.0]]))
-
     no_object_sim = _sim(scene_objects=0)
     no_object_sim._initialize_with_markers(None)
     no_object_sim.reset_envs(reset_state, object_state, env_ids)
@@ -431,6 +425,84 @@ def test_simulator_reset_and_park_env_paths_with_and_without_objects():
     assert torch.equal(no_object_sim.reset_calls[-1][2], torch.tensor([0, 1]))
 
 
+def test_move_all_envs_out_of_contact_and_base_flush_contact_cache():
+    sim = _sim(scene_objects=1)
+    sim._initialize_with_markers(None)
+    before = sim.get_root_state()
+    before_objects = sim.get_object_root_state()
+
+    sim._move_all_envs_out_of_contact()
+
+    moved, moved_objects, env_ids = sim.reset_calls[-1]
+    assert torch.equal(env_ids, torch.arange(sim.num_envs))
+    # Robots keep their own grid cell and rotation, sink to 50 m below ground in
+    # the default pose, at rest. Furniture goes 20 m further down so a robot
+    # cannot touch its own scene objects during the flush substep.
+    assert torch.equal(moved.root_pos[:, :2], before.root_pos[:, :2])
+    assert torch.equal(moved.root_pos[:, 2], torch.full((sim.num_envs,), -50.0))
+    assert torch.equal(moved.root_rot, before.root_rot)
+    assert torch.equal(moved.root_vel, torch.zeros(sim.num_envs, 3))
+    assert torch.equal(moved.root_ang_vel, torch.zeros(sim.num_envs, 3))
+    # The default pose is [0.25, -0.5] in the model's joint order; this fake
+    # simulator lists its joints the other way round, so seeing it reversed here
+    # shows the state was built in the common format and converted on the way in.
+    assert torch.equal(moved.dof_pos, torch.tensor([[-0.5, 0.25]] * sim.num_envs))
+    assert torch.equal(moved.dof_vel, torch.zeros_like(moved.dof_vel))
+    assert torch.equal(
+        moved_objects.root_pos[..., :2], before_objects.root_pos[..., :2]
+    )
+    assert torch.equal(
+        moved_objects.root_pos[..., 2], torch.full((sim.num_envs, 1), -70.0)
+    )
+    assert torch.equal(moved_objects.root_rot, before_objects.root_rot)
+    assert torch.equal(moved_objects.root_vel, torch.zeros_like(moved_objects.root_vel))
+    assert torch.equal(
+        moved_objects.root_ang_vel, torch.zeros_like(moved_objects.root_ang_vel)
+    )
+
+    no_object_sim = _sim(scene_objects=0)
+    no_object_sim._initialize_with_markers(None)
+    no_object_sim._move_all_envs_out_of_contact()
+    assert no_object_sim.reset_calls[-1][1] is None
+
+    # The base backend keeps no contacts between steps, so its flush must not
+    # move, step or reset anything.
+    reset_count = len(sim.reset_calls)
+    physics_steps = sim.physics_steps
+    assert sim.flush_contact_cache() is None
+    assert len(sim.reset_calls) == reset_count
+    assert sim.physics_steps == physics_steps
+
+
+def test_isaaclab_flush_contact_cache_simulates_one_substep_out_of_contact():
+    isaaclab_simulator = pytest.importorskip(
+        "protomotions.simulator.isaaclab.simulator"
+    )
+    events = []
+    fake = SimpleNamespace(
+        _move_all_envs_out_of_contact=lambda **kwargs: events.append(("move", kwargs)),
+        _scene=SimpleNamespace(
+            write_data_to_sim=lambda: events.append(("write_data_to_sim",)),
+            update=lambda dt: events.append(("update", dt)),
+        ),
+        _sim=SimpleNamespace(
+            step=lambda render: events.append(("step", render)),
+            get_physics_dt=lambda: 0.005,
+        ),
+    )
+
+    isaaclab_simulator.IsaacLabSimulator.flush_contact_cache(fake)
+
+    # Teleporting alone does not clear PhysX's contact memory; it takes one
+    # simulated substep with every robot (and its furniture) out of contact.
+    assert events == [
+        ("move", {"hide_z": -50.0, "object_drop": 20.0}),
+        ("write_data_to_sim",),
+        ("step", False),
+        ("update", 0.005),
+    ]
+
+
 def test_simulator_control_modes_and_domain_randomization_helpers():
     proportional = _sim(scene_objects=0, control_type=ControlType.PROPORTIONAL)
     proportional._initialize_with_markers(None)
diff --git a/protomotions/tests/test_cluster_retry_lib.py b/protomotions/tests/test_cluster_retry_lib.py
new file mode 100644
index 000000000..7f6aa9ffd
--- /dev/null
+++ b/protomotions/tests/test_cluster_retry_lib.py
@@ -0,0 +1,611 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Behavioural tests for scripts/cluster/retry_lib.sh -- the shared retry/watchdog
+primitives both train_slurm.py and lepton_train_loop.sh source.
+
+Everything is exercised with fake commands (a small script that bumps a counter,
+optionally writes an exception line to $LOGFILE, optionally advances $CKPT's mtime,
+and exits with a chosen code) so no GPU, cluster or real training is needed. The
+watchdog's poll interval and no-growth limit are overridden to sub-second values,
+and RETRY_BACKOFF_SEC is 0, so the suite runs in a couple of seconds.
+
+The three primitives under test:
+  kill_tree                 reaps a whole process tree.
+  run_with_stall_watchdog   THE one watchdog: kills a wedged tree on log silence.
+  run_with_retry            the unified loop: break (SLURM) / continue (Lepton) on
+                            checkpoint progress, fast-fail a repeated Python
+                            exception, bounded, optional give-up sentinel."""
+
+import os
+import shutil
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+import pytest
+
+
+RETRY_LIB = Path(__file__).resolve().parents[2] / "scripts" / "cluster" / "retry_lib.sh"
+
+pytestmark = pytest.mark.skipif(
+    sys.platform != "linux"
+    or shutil.which("bash") is None
+    or shutil.which("pgrep") is None,
+    reason="needs bash + pgrep + GNU date/stat/wc (the Linux CI runner has them)",
+)
+
+
+def _bash(body, env=None, timeout=40):
+    full = dict(os.environ)
+    if env:
+        full.update(env)
+    # Fail loudly if the lib cannot be sourced, so a wrong path can never look like
+    # a passing test (an undefined function would otherwise print EXIT=127).
+    snippet = f'source "{RETRY_LIB}" || {{ echo "SOURCE_FAILED"; exit 99; }}\n{body}'
+    return subprocess.run(
+        ["bash", "-c", snippet],
+        env=full,
+        capture_output=True,
+        text=True,
+        timeout=timeout,
+    )
+
+
+def _exit_code(stdout):
+    for line in stdout.splitlines():
+        if line.startswith("EXIT="):
+            return int(line.split("=", 1)[1])
+    raise AssertionError(f"no EXIT= line in:\n{stdout}")
+
+
+def _fake_cmd(tmp_path, name, body):
+    """A fake training command: bump the attempt counter into $n, record a RUN
+    line, then run `body` (which may read $n, write $LOGFILE/$CKPT, and exit)."""
+    p = tmp_path / name
+    p.write_text(
+        "#!/bin/bash\n"
+        'n=0; [ -f "$COUNTER" ] && n=$(cat "$COUNTER"); n=$((n + 1)); echo "$n" > "$COUNTER"\n'
+        'echo RUN >> "$RUNS"\n' + body + "\n"
+    )
+    p.chmod(0o755)
+    return p
+
+
+def _runs(tmp_path):
+    r = tmp_path / "runs"
+    return len(r.read_text().splitlines()) if r.exists() else 0
+
+
+def _env(tmp_path, log, **over):
+    e = {
+        "LOGFILE": str(log),
+        "CKPT": str(tmp_path / "last.ckpt"),
+        "COUNTER": str(tmp_path / "counter"),
+        "RUNS": str(tmp_path / "runs"),
+        "RETRY_MAX_ATTEMPTS": "3",
+        "RETRY_REPEAT_LIMIT": "3",
+        "RETRY_BACKOFF_SEC": "0",
+        "RETRY_ON_PROGRESS": "continue",
+    }
+    e.update({k: str(v) for k, v in over.items()})
+    return e
+
+
+def _run(tmp_path, cmd, env):
+    return _bash(f'run_with_retry "{cmd}"\necho "EXIT=$?"', env=env)
+
+
+# A body fragment that advances the checkpoint mtime to a distinct, increasing
+# value per attempt (second-granularity mtime can't otherwise change within a test
+# that runs in <1s). Used for the "training reached / made progress" attempts.
+_ADVANCE_CKPT = 'touch -m -d @$((1000000000 + n)) "$CKPT"'
+
+
+# --- run_with_retry: basic accounting ----------------------------------------
+
+
+def test_success_on_first_attempt_runs_once(tmp_path):
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(tmp_path, "ok.sh", "exit 0")
+    r = _run(tmp_path, cmd, _env(tmp_path, log))
+    assert _exit_code(r.stdout) == 0, r.stdout + r.stderr
+    assert _runs(tmp_path) == 1
+
+
+def test_passes_multiple_arguments_to_the_command(tmp_path):
+    # Production calls `run_with_retry run_with_stall_watchdog <python> -u ...` /
+    # `run_with_retry stdbuf -oL -eL srun ...` -- many argv elements. Run the whole
+    # argv, not just $1.
+    log = tmp_path / "log"
+    log.write_text("")
+    env = _env(tmp_path, log)
+    r = _bash(
+        'run_with_retry bash -c \'echo RUN >> "$RUNS"; exit 0\'\necho "EXIT=$?"',
+        env=env,
+    )
+    assert _exit_code(r.stdout) == 0, r.stdout + r.stderr
+    assert _runs(tmp_path) == 1
+
+
+def test_max_below_one_still_runs_once(tmp_path):
+    # A 0/negative cap must not skip the command and report a false success.
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(tmp_path, "ok.sh", "exit 0")
+    r = _run(tmp_path, cmd, _env(tmp_path, log, RETRY_MAX_ATTEMPTS=0))
+    assert _exit_code(r.stdout) == 0
+    assert _runs(tmp_path) == 1
+
+
+# --- break mode (SLURM): stop the moment training is reached ------------------
+
+
+def test_break_mode_stops_once_a_checkpoint_is_written(tmp_path):
+    # A scene build that reached training (wrote a checkpoint) then died -- like a
+    # walltime kill mid-run. In break mode the array element resumes from the
+    # checkpoint; retrying srun in place is wrong.
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(tmp_path, "midrun.sh", f'{_ADVANCE_CKPT}\nexit 1')
+    r = _run(
+        tmp_path, cmd, _env(tmp_path, log, RETRY_ON_PROGRESS="break", RETRY_MAX_ATTEMPTS=5)
+    )
+    assert _exit_code(r.stdout) == 1
+    assert _runs(tmp_path) == 1
+
+
+def test_break_mode_retries_pretraining_abort_up_to_the_cap(tmp_path):
+    # A native abort before training (no checkpoint, no Python traceback) is flaky:
+    # retry it, bounded by the cap, then return the last exit code.
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(tmp_path, "abort.sh", 'echo "double free or corruption" >> "$LOGFILE"\nexit 250')
+    r = _run(
+        tmp_path, cmd, _env(tmp_path, log, RETRY_ON_PROGRESS="break", RETRY_MAX_ATTEMPTS=4)
+    )
+    assert _exit_code(r.stdout) == 250
+    assert _runs(tmp_path) == 4
+
+
+# --- continue mode (Lepton): resume until done -------------------------------
+
+
+def test_continue_mode_resumes_on_progress_then_completes(tmp_path):
+    log = tmp_path / "log"
+    log.write_text("")
+    # Attempt 1 advances the checkpoint then crashes; attempt 2 completes.
+    cmd = _fake_cmd(
+        tmp_path, "prog.sh", f'if [ "$n" -eq 1 ]; then {_ADVANCE_CKPT}; exit 1; fi\nexit 0'
+    )
+    r = _run(
+        tmp_path, cmd, _env(tmp_path, log, RETRY_ON_PROGRESS="continue", RETRY_MAX_ATTEMPTS=2)
+    )
+    assert _exit_code(r.stdout) == 0, r.stdout + r.stderr
+    assert _runs(tmp_path) == 2
+
+
+def test_continue_mode_gives_up_after_consecutive_no_progress(tmp_path):
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(tmp_path, "fail.sh", "exit 1")
+    r = _run(
+        tmp_path,
+        cmd,
+        _env(
+            tmp_path,
+            log,
+            RETRY_ON_PROGRESS="continue",
+            RETRY_MAX_ATTEMPTS=2,
+            RETRY_TOTAL_ATTEMPTS=9,
+            GAVE_UP=str(tmp_path / "gave_up"),
+        ),
+    )
+    assert _exit_code(r.stdout) == 1
+    assert _runs(tmp_path) == 2  # gave up at the no-progress cap, not 9
+    assert (tmp_path / "gave_up").exists(), "give-up sentinel not written"
+
+
+def test_continue_mode_total_cap_bounds_a_never_completing_run(tmp_path):
+    # Every attempt advances the checkpoint (progress) then crashes, so the
+    # no-progress streak keeps resetting and never trips its cap. RETRY_TOTAL_ATTEMPTS
+    # is the backstop that stops it looping forever; and because it exhausted the
+    # budget while still making progress (not deterministic), NO sentinel is written.
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(tmp_path, "slowprog.sh", f'{_ADVANCE_CKPT}\nexit 1')
+    r = _run(
+        tmp_path,
+        cmd,
+        _env(
+            tmp_path,
+            log,
+            RETRY_ON_PROGRESS="continue",
+            RETRY_MAX_ATTEMPTS=2,
+            RETRY_TOTAL_ATTEMPTS=4,
+            GAVE_UP=str(tmp_path / "gave_up"),
+        ),
+    )
+    assert _exit_code(r.stdout) == 1
+    assert _runs(tmp_path) == 4
+    assert not (tmp_path / "gave_up").exists(), "sentinel written despite progress"
+
+
+def test_prior_give_up_sentinel_short_circuits_without_running(tmp_path):
+    log = tmp_path / "log"
+    log.write_text("")
+    gave_up = tmp_path / "gave_up"
+    gave_up.write_text("")
+    cmd = _fake_cmd(tmp_path, "ok.sh", "exit 0")
+    r = _run(tmp_path, cmd, _env(tmp_path, log, GAVE_UP=str(gave_up)))
+    assert _exit_code(r.stdout) != 0, r.stdout + r.stderr  # not a false success
+    assert _runs(tmp_path) == 0, "command ran despite the give-up sentinel"
+
+
+# --- the Python fast-fail: retry flaky, refuse deterministic -----------------
+
+
+def test_repeated_identical_exception_fast_fails(tmp_path):
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(tmp_path, "det.sh", 'echo "AssertionError: boom" >> "$LOGFILE"\nexit 1')
+    r = _run(
+        tmp_path,
+        cmd,
+        _env(tmp_path, log, RETRY_MAX_ATTEMPTS=10, RETRY_REPEAT_LIMIT=3, GAVE_UP=str(tmp_path / "g")),
+    )
+    assert _exit_code(r.stdout) == 1
+    assert _runs(tmp_path) == 3  # stopped at the repeat limit, not 10
+    assert "deterministic, not flaky" in r.stderr
+    assert "AssertionError: boom" in r.stderr  # the traceback is surfaced
+    assert (tmp_path / "g").exists()  # a deterministic give-up writes the sentinel
+
+
+def test_message_less_exception_fast_fails(tmp_path):
+    # A bare `assert` prints just "AssertionError" -- no colon, no message.
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(tmp_path, "bare.sh", 'echo "AssertionError" >> "$LOGFILE"\nexit 1')
+    r = _run(tmp_path, cmd, _env(tmp_path, log, RETRY_MAX_ATTEMPTS=10, RETRY_REPEAT_LIMIT=3))
+    assert _exit_code(r.stdout) == 1
+    assert _runs(tmp_path) == 3
+    assert "deterministic, not flaky" in r.stderr
+
+
+def test_same_class_varying_message_fast_fails(tmp_path):
+    # Same class, different message each attempt (a device id): the bug is one, so
+    # the class-only comparison must still count it.
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(tmp_path, "vary.sh", 'echo "RuntimeError: CUDA error on device $n" >> "$LOGFILE"\nexit 1')
+    r = _run(tmp_path, cmd, _env(tmp_path, log, RETRY_MAX_ATTEMPTS=10, RETRY_REPEAT_LIMIT=3))
+    assert _exit_code(r.stdout) == 1
+    assert _runs(tmp_path) == 3
+
+
+def test_native_abort_without_traceback_uses_full_budget(tmp_path):
+    # exit 250 with an abort message but NO Python exception line -> the fast-fail
+    # must not trip; this is the heap-corruption bug and it must keep retrying.
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(tmp_path, "abort.sh", 'echo "double free or corruption (out)" >> "$LOGFILE"\nexit 250')
+    r = _run(tmp_path, cmd, _env(tmp_path, log, RETRY_MAX_ATTEMPTS=4, RETRY_REPEAT_LIMIT=3))
+    assert _exit_code(r.stdout) == 250
+    assert _runs(tmp_path) == 4
+
+
+def test_alternating_exceptions_do_not_fast_fail(tmp_path):
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(
+        tmp_path,
+        "alt.sh",
+        'if [ $((n % 2)) -eq 1 ]; then echo "ValueError: odd" >> "$LOGFILE"; '
+        'else echo "KeyError: even" >> "$LOGFILE"; fi\nexit 1',
+    )
+    r = _run(tmp_path, cmd, _env(tmp_path, log, RETRY_MAX_ATTEMPTS=5, RETRY_REPEAT_LIMIT=3))
+    assert _exit_code(r.stdout) == 1
+    assert _runs(tmp_path) == 5  # never two identical in a row -> full budget
+
+
+def test_fast_fail_reads_only_the_current_attempt_slice(tmp_path):
+    # Odd attempts emit the exception, even attempts emit nothing. Only if each
+    # attempt's exception is read from ITS OWN log slice does this stay under the
+    # limit -- reading the whole accumulated log would see the previous attempt's
+    # line during a silent attempt and stop early. Guards the _mark slicing.
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(
+        tmp_path, "slice.sh", 'case "$n" in 1|3|5) echo "ValueError: boom" >> "$LOGFILE";; esac\nexit 1'
+    )
+    r = _run(tmp_path, cmd, _env(tmp_path, log, RETRY_MAX_ATTEMPTS=5, RETRY_REPEAT_LIMIT=2))
+    assert _exit_code(r.stdout) == 1
+    assert _runs(tmp_path) == 5  # never two same-class in a row -> whole budget
+
+
+def test_progress_resets_the_repeat_counter(tmp_path):
+    # A deterministic-looking exception on either side of a real progress step must
+    # NOT accumulate across the progress: the reset of prev_cls/repeat on progress
+    # means attempts 1 and 3 (same class, but with progress between them) do not
+    # fast-fail. With REPEAT_LIMIT=2 and a progress step at attempt 2, the run
+    # survives to completion at attempt 4.
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(
+        tmp_path,
+        "reset.sh",
+        "case \"$n\" in\n"
+        '  1) echo "ValueError: x" >> "$LOGFILE"; exit 1 ;;\n'  # no progress, exc
+        f'  2) {_ADVANCE_CKPT}; exit 1 ;;\n'  # progress -> resets counters
+        '  3) echo "ValueError: x" >> "$LOGFILE"; exit 1 ;;\n'  # no progress, same class again
+        f'  4) {_ADVANCE_CKPT}; exit 0 ;;\n'  # completes
+        "esac",
+    )
+    r = _run(
+        tmp_path,
+        cmd,
+        _env(
+            tmp_path,
+            log,
+            RETRY_ON_PROGRESS="continue",
+            RETRY_MAX_ATTEMPTS=2,
+            RETRY_REPEAT_LIMIT=2,
+            RETRY_TOTAL_ATTEMPTS=9,
+            GAVE_UP=str(tmp_path / "g"),
+        ),
+    )
+    assert _exit_code(r.stdout) == 0, r.stdout + r.stderr
+    assert not (tmp_path / "g").exists(), "fast-failed despite progress between the two"
+
+
+def test_one_time_epoch0_checkpoint_does_not_defeat_fast_fail(tmp_path):
+    # Models base_agent's epoch-0 save: last.ckpt is created ONCE on the first
+    # attempt (advancing the mtime), then a deterministic bug crashes every attempt
+    # with the same exception and NO further checkpoint progress (base_agent writes
+    # the epoch-0 ckpt only when absent, so resume attempts do not re-touch it). The
+    # single early "progress" must not mask the deterministic crash -- it fast-fails a
+    # couple attempts later, not after the whole 75-run budget. This is the contract
+    # base_agent's only-if-absent save relies on; the unconditional save it replaced
+    # advanced the mtime EVERY attempt and burned the whole budget instead.
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(
+        tmp_path,
+        "epoch0.sh",
+        f'if [ "$n" -eq 1 ]; then {_ADVANCE_CKPT}; fi\n'
+        'echo "RuntimeError: boom" >> "$LOGFILE"\nexit 1',
+    )
+    r = _run(
+        tmp_path,
+        cmd,
+        _env(
+            tmp_path,
+            log,
+            RETRY_ON_PROGRESS="continue",
+            RETRY_MAX_ATTEMPTS=9,
+            RETRY_REPEAT_LIMIT=3,
+            RETRY_TOTAL_ATTEMPTS=75,
+            GAVE_UP=str(tmp_path / "g"),
+        ),
+    )
+    assert _exit_code(r.stdout) == 1
+    # attempt 1: progress -> reset. attempts 2,3,4: same class -> repeat 1,2,3 -> stop.
+    assert _runs(tmp_path) == 4, r.stdout + r.stderr
+    assert (tmp_path / "g").exists()
+
+
+def test_total_attempts_below_no_progress_cap_is_honored(tmp_path):
+    # An explicit small total (Lepton --max-runs below --no-progress-limit) is a hard
+    # cap: the loop stops at total, not silently at the larger no-progress cap. Guards
+    # the clamp that used to raise total up to nomax.
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(tmp_path, "fail.sh", "exit 1")
+    r = _run(
+        tmp_path,
+        cmd,
+        _env(
+            tmp_path,
+            log,
+            RETRY_ON_PROGRESS="continue",
+            RETRY_MAX_ATTEMPTS=3,
+            RETRY_TOTAL_ATTEMPTS=2,
+        ),
+    )
+    assert _exit_code(r.stdout) == 1
+    assert _runs(tmp_path) == 2  # honored total=2, not the no-progress cap of 3
+
+
+# --- run_with_stall_watchdog + kill_tree (THE one watchdog) ------------------
+
+
+def test_watchdog_kills_a_silent_child(tmp_path):
+    log = tmp_path / "log"
+    log.write_text("")  # never grows
+    env = {"LOGFILE": str(log), "STALL_POLL_SEC": "0.05", "STALL_QUIET_LIMIT": "3"}
+    r = _bash('run_with_stall_watchdog sleep 100\necho "EXIT=$?"', env=env, timeout=15)
+    assert _exit_code(r.stdout) != 0, r.stdout + r.stderr
+
+
+def test_watchdog_lets_an_active_child_finish(tmp_path):
+    log = tmp_path / "log"
+    log.write_text("")
+    env = {"LOGFILE": str(log), "STALL_POLL_SEC": "0.05", "STALL_QUIET_LIMIT": "4"}
+    body = (
+        "run_with_stall_watchdog bash -c "
+        "'for i in $(seq 1 15); do echo tick >> \"$LOGFILE\"; sleep 0.03; done'\n"
+        'echo "EXIT=$?"'
+    )
+    r = _bash(body, env=env, timeout=15)
+    assert _exit_code(r.stdout) == 0, r.stdout + r.stderr
+
+
+def test_failed_attempt_leaves_no_orphaned_ranks(tmp_path):
+    """A failed attempt must not leave its surviving ranks running.
+
+    When one rank aborts, the launcher does not always reap its siblings; those
+    survivors reparent to init, keeping their GPU contexts, their MASTER_PORT and
+    -- as seen on a real Lepton job -- spamming NCCL errors once a second through
+    every later attempt. kill_tree cannot reach them (its pgrep -P walk stops at
+    the vanished parent), so the attempt runs in its own process group and the
+    group is reaped afterwards.
+
+    Shape mirrors production: run_with_retry wraps run_with_stall_watchdog, which
+    is where the process group is established (lepton_train_loop.sh does exactly
+    this).
+    """
+    marker = tmp_path / "rank"
+    log = tmp_path / "log"
+    log.write_text("")
+    env = {
+        "LOGFILE": str(log),
+        "MARK": str(marker),
+        "CKPT": str(tmp_path / "nonexistent.ckpt"),
+        "RETRY_MAX_ATTEMPTS": "2",
+        "RETRY_BACKOFF_SEC": "0",
+        "RETRY_REPEAT_LIMIT": "0",
+        "STALL_QUIET_LIMIT": "0",  # watchdog off: isolate the crash path
+        "REAP_GRACE_SEC": "1",
+    }
+    # Three "ranks" that outlive the attempt; the attempt then exits nonzero.
+    body = (
+        "attempt() {\n"
+        "  for r in 1 2 3; do\n"
+        '    ( for i in $(seq 1 100); do echo alive >> "${MARK}.$r"; sleep 0.2; done ) &\n'
+        "  done\n"
+        "  sleep 0.4\n"
+        "  exit 250\n"
+        "}\n"
+        "export -f attempt\n"
+        "run_with_retry run_with_stall_watchdog bash -c attempt >/dev/null 2>&1\n"
+        'echo "EXIT=$?"\n'
+    )
+    r = _bash(body, env=env, timeout=60)
+    # Sanity: the ranks really did start, so a green result cannot mean "nothing ran".
+    assert any(
+        (tmp_path / f"rank.{i}").exists() for i in (1, 2, 3)
+    ), f"no rank markers written; test did not exercise the path: {r.stdout}{r.stderr}"
+
+    counts = {}
+    for i in (1, 2, 3):
+        p = tmp_path / f"rank.{i}"
+        counts[i] = len(p.read_text().splitlines()) if p.exists() else 0
+    time.sleep(1.0)
+    still_running = []
+    for i in (1, 2, 3):
+        p = tmp_path / f"rank.{i}"
+        now = len(p.read_text().splitlines()) if p.exists() else 0
+        if now > counts[i]:
+            still_running.append(i)
+    assert not still_running, (
+        f"ranks {still_running} outlived the retry loop -- orphan leak. "
+        f"{r.stdout}{r.stderr}"
+    )
+
+
+def test_watchdog_kills_a_child_emitting_only_dead_rank_noise(tmp_path):
+    """A half-dead DDP job must be killed even though its log keeps growing.
+
+    When a rank DIES (rather than wedging), the survivors stop blocking at the
+    collective and emit NCCL heartbeat / TCPStore "Broken pipe" errors several
+    lines a second, forever. A watchdog keyed on raw file size therefore never
+    fires -- the symptom of the failure is what keeps it asleep. Measured on a
+    real 4-rank job whose ranks 0 and 1 vanished silently at epoch 1; it would
+    have idled 4 A100s for the full job TTL.
+    """
+    log = tmp_path / "log"
+    log.write_text("")
+    env = {"LOGFILE": str(log), "STALL_POLL_SEC": "0.05", "STALL_QUIET_LIMIT": "3"}
+    # Keep the payload free of quotes: it is nested inside bash -c '...' and a
+    # stray quote makes the CHILD die of a syntax error, which would make this
+    # test pass for the wrong reason (it did, on the first draft).
+    body = (
+        "run_with_stall_watchdog bash -c "
+        "'for i in $(seq 1 400); do "
+        "echo [rank2]: ProcessGroupNCCL Broken pipe should dump >> \"$LOGFILE\"; "
+        "sleep 0.02; done'\n"
+        'echo "EXIT=$?"'
+    )
+    r = _bash(body, env=env, timeout=30)
+    # Guard against the child having failed for its own reasons: it must have
+    # actually written the noise before being killed.
+    assert log.read_text().count("ProcessGroupNCCL") > 5, log.read_text()[:400]
+    assert _exit_code(r.stdout) != 0, r.stdout + r.stderr
+
+
+def test_watchdog_spares_a_child_mixing_progress_with_noise(tmp_path):
+    """Noise must not mask real progress: a job still logging epochs survives."""
+    log = tmp_path / "log"
+    log.write_text("")
+    env = {"LOGFILE": str(log), "STALL_POLL_SEC": "0.05", "STALL_QUIET_LIMIT": "4"}
+    body = (
+        "run_with_stall_watchdog bash -c "
+        "'for i in $(seq 1 15); do "
+        'echo "[rank2]: ProcessGroupNCCL Broken pipe" >> "$LOGFILE"; '
+        'echo "Epoch $i, training... 100% 0:00:47" >> "$LOGFILE"; '
+        "sleep 0.03; done'\n"
+        'echo "EXIT=$?"'
+    )
+    r = _bash(body, env=env, timeout=15)
+    assert _exit_code(r.stdout) == 0, r.stdout + r.stderr
+
+
+@pytest.mark.parametrize("disable_env", ["STALL_QUIET_LIMIT=0", "STALL_TIMEOUT_MIN=0"])
+def test_watchdog_disabled_does_not_kill(tmp_path, disable_env):
+    log = tmp_path / "log"
+    log.write_text("")
+    key, val = disable_env.split("=")
+    env = {"LOGFILE": str(log), "STALL_POLL_SEC": "0.05", key: val}
+    r = _bash(
+        "run_with_stall_watchdog bash -c 'sleep 0.4'\n" 'echo "EXIT=$?"',
+        env=env,
+        timeout=15,
+    )
+    assert _exit_code(r.stdout) == 0, r.stdout + r.stderr
+
+
+def test_kill_tree_reaps_the_whole_subtree(tmp_path):
+    pids = tmp_path / "pids"
+    body = (
+        f'export PIDS="{pids}"\n'
+        ': > "$PIDS"\n'
+        "bash -c 'sleep 100 & echo $! >> \"$PIDS\"; sleep 100 & echo $! >> \"$PIDS\"; wait' &\n"
+        "root=$!\n"
+        "sleep 0.4\n"
+        'kill_tree "$root"\n'
+        "sleep 0.3\n"
+        "alive=0\n"
+        'while read -r p; do kill -0 "$p" 2>/dev/null && alive=$((alive+1)); done < "$PIDS"\n'
+        'kill -0 "$root" 2>/dev/null && alive=$((alive+1))\n'
+        'echo "ALIVE=$alive"'
+    )
+    r = _bash(body, timeout=15)
+    assert "ALIVE=0" in r.stdout, r.stdout + r.stderr
+
+
+def test_watchdog_inside_retry_kills_hang_then_retry_succeeds(tmp_path):
+    # The production composition: run_with_retry wraps run_with_stall_watchdog. A
+    # hung first attempt (silent forever) is killed by the watchdog, run_with_retry
+    # sees the nonzero exit and -- no checkpoint, no exception -> flaky -- retries;
+    # the second attempt completes.
+    log = tmp_path / "log"
+    log.write_text("")
+    cmd = _fake_cmd(
+        tmp_path, "hang.sh", 'case "$n" in 1) sleep 100;; *) exit 0;; esac'
+    )
+    env = _env(
+        tmp_path,
+        log,
+        RETRY_MAX_ATTEMPTS=3,
+        STALL_POLL_SEC="0.1",
+        STALL_QUIET_LIMIT="3",
+    )
+    r = _bash(
+        f'run_with_retry run_with_stall_watchdog "{cmd}"\necho "EXIT=$?"',
+        env=env,
+        timeout=30,
+    )
+    assert _exit_code(r.stdout) == 0, r.stdout + r.stderr
+    assert _runs(tmp_path) == 2  # attempt 1 killed by the watchdog, attempt 2 ok
diff --git a/protomotions/tests/test_common_module_container.py b/protomotions/tests/test_common_module_container.py
index e9e0eaa18..3c0acfa90 100644
--- a/protomotions/tests/test_common_module_container.py
+++ b/protomotions/tests/test_common_module_container.py
@@ -3,12 +3,18 @@
 
 """Tests for generic TensorDict module containers."""
 
+from types import SimpleNamespace
+
 import torch
 import pytest
 from tensordict import TensorDict
 from tensordict.nn import TensorDictModuleBase
 
-from protomotions.agents.base_agent.model import BaseModel, ProtoMotionsTensorDictModule
+from protomotions.agents.base_agent.model import (
+    BaseModel,
+    ProtoMotionsTensorDictModule,
+    RolloutStateSpec,
+)
 from protomotions.agents.common.common import ModuleContainer
 from protomotions.agents.common.config import ModuleContainerConfig
 
@@ -54,6 +60,21 @@ def forward(self, tensordict: TensorDict):
         return tensordict
 
 
+class _EpisodeNoiseModule(ProtoMotionsTensorDictModule):
+    def __init__(self, config):
+        super().__init__()
+        self.in_keys = ["obs"]
+        self.out_keys = ["latent"]
+
+    def rollout_state_specs(self):
+        return {"episode_noise": RolloutStateSpec(shape=(2,), init="normal")}
+
+    def forward(self, tensordict: TensorDict):
+        self.read_rollout_state(tensordict)
+        tensordict["latent"] = tensordict["obs"] + tensordict["episode_noise"]
+        return tensordict
+
+
 class _PlainTensorDictModule(TensorDictModuleBase):
     def __init__(self, config):
         super().__init__()
@@ -150,6 +171,35 @@ def test_module_container_propagates_reset_to_internal_common_modules():
     assert container.models[0].reset_calls == [(env_ids, 3, "cpu")]
 
 
+def test_saved_rollout_context_restores_nested_module_state_after_reseeding():
+    container = ModuleContainer(
+        ModuleContainerConfig(
+            in_keys=["obs"],
+            out_keys=["plain_out"],
+            models=[
+                ModuleContainerConfig(
+                    _target_="protomotions.tests.test_common_module_container._EpisodeNoiseModule",
+                ),
+                ModuleContainerConfig(
+                    _target_="protomotions.tests.test_common_module_container._PlainTensorDictModule",
+                ),
+            ],
+        )
+    )
+    torch.manual_seed(0)
+    container.reset_rollout_context(num_envs=3, device="cpu")
+    noise = container.models[0].episode_noise
+    before = noise.clone()
+
+    saved = container.save_rollout_context()
+    container.reset_rollout_context(env_ids=torch.tensor([0, 2]))
+    assert not torch.equal(noise, before)
+    container.load_rollout_context(saved)
+
+    assert list(saved) == ["models.0.episode_noise"]
+    assert torch.equal(noise, before)
+
+
 def test_base_model_experience_buffer_keys_include_rollout_context_keys_once():
     model = _StatefulModel(config=None)
     model.out_keys = ["action", "latent_noise"]
@@ -198,3 +248,88 @@ def test_module_container_rejects_promised_outputs_not_produced():
                 ],
             )
         )
+
+
+# ---------------------------------------------------------------------------
+# Module ordering: `models` is execution order, not a dependency graph
+# ---------------------------------------------------------------------------
+
+
+def _stub(in_keys, out_keys):
+    """A config-shaped stand-in; find_key_flow_errors reads only in/out keys."""
+    return SimpleNamespace(in_keys=list(in_keys), out_keys=list(out_keys))
+
+
+def test_key_flow_accepts_a_correctly_ordered_list():
+    config = ModuleContainerConfig(
+        in_keys=["obs"],
+        out_keys=["action"],
+        models=[_stub(["obs"], ["token"]), _stub(["token"], ["action"])],
+    )
+    assert config.find_key_flow_errors() == []
+
+
+def test_key_flow_reports_a_producer_that_runs_after_its_consumer():
+    """The real defect: the producer was appended to the end of the list.
+
+    A scene-conditioning channel was added by appending its normalizer and token
+    MLP after the transformer that consumes the token. The names lined up, so it
+    read as correct; the container validates in list order, so it was not.
+    """
+    config = ModuleContainerConfig(
+        in_keys=["obs"],
+        out_keys=["action"],
+        models=[
+            _stub(["obs", "scene_token"], ["action"]),  # consumer, first
+            _stub(["scene_obs"], ["scene_norm"]),  # producers, appended after
+            _stub(["scene_norm"], ["scene_token"]),
+        ],
+    )
+
+    errors = config.find_key_flow_errors()
+
+    assert len(errors) == 1
+    assert "'scene_token'" in errors[0]
+    assert "model 2" in errors[0]
+    assert "execution order" in errors[0]
+
+
+def test_key_flow_stays_quiet_about_keys_nothing_declares():
+    """Some modules derive their real keys at construction, so silence is correct.
+
+    DiscreteLatent takes its in_keys from its encoder rather than from config, so
+    "no module declares this output" does not imply the key is missing. Only a
+    known later producer is unambiguous, and only that is reported.
+    """
+    config = ModuleContainerConfig(
+        in_keys=[],
+        out_keys=["action"],
+        models=[_stub(["produced_at_runtime"], ["action"])],
+    )
+    assert config.find_key_flow_errors() == []
+
+
+def test_container_rejects_a_misordered_list_before_building_any_module():
+    class _Exploding:
+        def __init__(self, config):
+            raise AssertionError("no module should be constructed")
+
+    config = ModuleContainerConfig(
+        in_keys=["obs"],
+        out_keys=["action"],
+        models=[
+            SimpleNamespace(
+                in_keys=["late_token"],
+                out_keys=["action"],
+                _target_=f"{__name__}._Exploding",
+            ),
+            SimpleNamespace(
+                in_keys=["obs"],
+                out_keys=["late_token"],
+                _target_=f"{__name__}._Exploding",
+            ),
+        ],
+    )
+
+    with pytest.raises(AssertionError, match="out of order"):
+        ModuleContainer(config)
diff --git a/protomotions/tests/test_context_views_and_component_factories.py b/protomotions/tests/test_context_views_and_component_factories.py
index d45f385bc..160ef9bda 100644
--- a/protomotions/tests/test_context_views_and_component_factories.py
+++ b/protomotions/tests/test_context_views_and_component_factories.py
@@ -343,6 +343,13 @@ def test_observation_factories_bind_expected_context_paths():
 
 
 def test_reward_factories_and_bundles_bind_expected_context_paths():
+    rate = factories.action_rate_l2_factory()
+    assert _bindings(rate) == {
+        "raw_action": "current_action",
+        "previous_raw_action": "previous_action",
+    }
+    assert _params(rate)["weight"] == -0.1
+
     smooth = factories.action_smoothness_factory(weight=-0.5)
     assert _bindings(smooth)["current_processed_action"] == "current_processed_action"
     assert _params(smooth)["weight"] == -0.5
diff --git a/protomotions/tests/test_example_experiment_configs.py b/protomotions/tests/test_example_experiment_configs.py
index 3e3a4d47b..8520d989c 100644
--- a/protomotions/tests/test_example_experiment_configs.py
+++ b/protomotions/tests/test_example_experiment_configs.py
@@ -22,6 +22,7 @@
 from examples.experiments.gpc import target_prior_peft
 from examples.experiments.gpc import task_target_prior_peft_amp
 from examples.experiments.mimic import fsq as mimic_fsq
+from protomotions.agents.common.config import ModuleContainerConfig
 from protomotions.agents.common.latent import LATENT_KEY, LATENT_LOGITS_KEY
 from protomotions.agents.supervised.config import RolloutActor
 
@@ -64,6 +65,32 @@ def _args(**overrides):
     return argparse.Namespace(**values)
 
 
+
+def assert_module_order_is_sound(config, _seen=None):
+    """Walk every ModuleContainerConfig under `config` and check module ordering.
+
+    `models` is execution order, so a producer appended after its consumer never
+    runs in time. That is invisible until the container is built, which on a
+    multi-rank job means one rank raising and the others deadlocking at the next
+    collective -- an hour of cluster time to learn what this costs milliseconds.
+    """
+    _seen = _seen if _seen is not None else set()
+    if id(config) in _seen:
+        return
+    _seen.add(id(config))
+
+    if isinstance(config, ModuleContainerConfig):
+        errors = config.find_key_flow_errors()
+        assert not errors, "module ordering error:\n  " + "\n  ".join(errors)
+
+    children = config if isinstance(config, (list, tuple)) else None
+    if children is None:
+        children = list(vars(config).values()) if hasattr(config, "__dict__") else []
+    for child in children:
+        if isinstance(child, (list, tuple)) or hasattr(child, "__dict__"):
+            assert_module_order_is_sound(child, _seen)
+
+
 def test_mimic_fsq_basic_config_factories_and_inference_overrides():
     args = _args()
     robot_cfg = _RobotConfig()
@@ -74,6 +101,7 @@ def test_mimic_fsq_basic_config_factories_and_inference_overrides():
     motion_cfg = mimic_fsq.motion_lib_config(args)
     env_cfg = mimic_fsq.env_config(robot_cfg, args)
     agent_cfg = mimic_fsq.agent_config(robot_cfg, env_cfg, args)
+    assert_module_order_is_sound(agent_cfg)
 
     assert terrain_cfg.__class__.__name__ == "TerrainConfig"
     assert scene_cfg.scene_file == "scenes.pt"
@@ -162,6 +190,7 @@ def test_gpc_prior_parser_and_checkpoint_dependent_config(monkeypatch):
     motion_cfg = gpc_prior.motion_lib_config(args)
     env_cfg = gpc_prior.env_config(robot_cfg, args)
     agent_cfg = gpc_prior.agent_config(robot_cfg, env_cfg, args)
+    assert_module_order_is_sound(agent_cfg)
 
     assert terrain_cfg.__class__.__name__ == "TerrainConfig"
     assert scene_cfg.scene_file == "scenes.pt"
@@ -220,6 +249,7 @@ def fake_load(path):
     assert gpc_prior._tracker_future_steps(_args()) == [2, 5, 9]
 
     agent_cfg = gpc_prior.agent_config(_RobotConfig(), SimpleNamespace(), _args())
+    assert_module_order_is_sound(agent_cfg)
     decoder_cfg = agent_cfg.model.latent_decoder
     assert decoder_cfg.module_config_path == gpc_prior.TRACKER_MODULE_CONFIG_PATH
     assert decoder_cfg.module_config is None
diff --git a/protomotions/tests/test_inference_agent_script.py b/protomotions/tests/test_inference_agent_script.py
index 9e264baaa..e9c7a1652 100644
--- a/protomotions/tests/test_inference_agent_script.py
+++ b/protomotions/tests/test_inference_agent_script.py
@@ -92,6 +92,7 @@ def _write_resolved_configs(run_dir):
         _target_="protomotions.simulator.isaacgym.simulator.IsaacGymSimulator",
         num_envs=1,
         headless=False,
+        sim=SimpleNamespace(fps=60, decimation=2),
     )
     terrain_config = SimpleNamespace(name="terrain")
     scene_lib_config = SimpleNamespace(scene_file="old_scene.yaml", asset_root="/old/root")
@@ -523,9 +524,16 @@ def fake_build_all_components(**kwargs):
             _target_="protomotions.simulator.newton.simulator.NewtonSimulator",
             num_envs=current_simulator_config.num_envs,
             headless=current_simulator_config.headless,
+            sim=SimpleNamespace(fps=120, decimation=4),
             switched_to=new_simulator,
         )
     )
+    rate_checks = []
+
+    def record_rate_check(train_sim, sim, name):
+        rate_checks.append((train_sim.fps, sim.fps, name))
+
+    simulator_factory.warn_on_control_rate_change = record_rate_check
     config_utils = ModuleType("protomotions.utils.config_utils")
     config_utils.parse_cli_overrides = lambda overrides: {"env.foo": 1}
     config_utils.apply_config_overrides = (
@@ -574,6 +582,7 @@ def fake_build_all_components(**kwargs):
     assert build_call[1]["simulator_config"].num_envs == 3
     assert build_call[1]["simulator_config"].headless is True
     assert build_call[1]["simulator_config"].switched_to == "newton"
+    assert rate_checks == [(60, 120, "newton")]
     assert build_call[1]["motion_lib_config"].motion_file == "override.motion"
     assert build_call[1]["scene_lib_config"].scene_file is None
     assert build_call[1]["scene_lib_config"].asset_root is None
diff --git a/protomotions/tests/test_lepton_launcher.py b/protomotions/tests/test_lepton_launcher.py
index 8e8058074..77a11b01a 100644
--- a/protomotions/tests/test_lepton_launcher.py
+++ b/protomotions/tests/test_lepton_launcher.py
@@ -59,6 +59,7 @@ def _args(**over):
         registry_secret=None,
         stall_timeout_min=30,
         no_progress_limit=3,
+        repeat_limit=3,
         shared_memory_mib=65536,
         preemptible=False,
         max_job_retries=10,
@@ -167,12 +168,22 @@ def test_lep_job_create_forwards_disabled_stall_timeout():
 
 
 def test_lep_job_create_passes_no_progress_limit_env():
-    # lepton_train_loop.sh reads NO_PROGRESS_LIMIT; the launcher must let a flaky
-    # scene build raise it above the default 3, or three ordinary aborts kill the job.
+    # lepton_train_loop.sh maps --no-progress-limit onto RETRY_MAX_ATTEMPTS (the
+    # shared run_with_retry knob); the launcher must let a flaky scene build raise it
+    # above the default 3, or three ordinary aborts kill the job.
     cmd = tl.build_lep_job_create(
         _args(no_progress_limit=8), _site(), "/mnt/exp", "true"
     )
-    assert "NO_PROGRESS_LIMIT=8" in cmd
+    assert "RETRY_MAX_ATTEMPTS=8" in cmd
+
+
+def test_lep_job_create_passes_repeat_limit_env():
+    # --repeat-limit feeds RETRY_REPEAT_LIMIT: the fast-fail-on-python threshold the
+    # Lepton loop previously lacked. The launcher must forward it.
+    cmd = tl.build_lep_job_create(
+        _args(repeat_limit=5), _site(), "/mnt/exp", "true"
+    )
+    assert "RETRY_REPEAT_LIMIT=5" in cmd
 
 
 def test_lep_job_create_missing_image_fails_cleanly():
diff --git a/protomotions/tests/test_lepton_train_loop.py b/protomotions/tests/test_lepton_train_loop.py
index 35b768056..4009b561c 100644
--- a/protomotions/tests/test_lepton_train_loop.py
+++ b/protomotions/tests/test_lepton_train_loop.py
@@ -15,7 +15,7 @@
                                 IsaacLab warmup, so a fake python drives it.
 
 The watchdog poll interval (STALL_POLL_SEC) and no-growth limit (STALL_QUIET_LIMIT
-/ NO_PROGRESS_LIMIT) are overridden to sub-second / tiny values, so the suite runs
+/ RETRY_MAX_ATTEMPTS) are overridden to sub-second / tiny values, so the suite runs
 in a couple of seconds rather than minutes."""
 
 import os
@@ -225,7 +225,7 @@ def test_deterministic_failure_gives_up_and_writes_sentinel(tmp_path):
     exp = tmp_path / "exp"
     exp.mkdir()
     r = _run_loop(
-        always_fail, exp, 5, ["--robot-name=g1"], extra_env={"NO_PROGRESS_LIMIT": "2"}
+        always_fail, exp, 5, ["--robot-name=g1"], extra_env={"RETRY_MAX_ATTEMPTS": "2"}
     )
     assert r.returncode != 0, r.stdout + r.stderr  # the job is marked failed
     assert (exp / ".lepton_loop_gave_up").exists(), "give-up sentinel not written"
@@ -247,7 +247,7 @@ def test_crash_after_progress_then_success(tmp_path):
     exp = tmp_path / "exp"
     exp.mkdir()
     r = _run_loop(
-        prog, exp, 5, ["--robot-name=g1"], extra_env={"NO_PROGRESS_LIMIT": "2"}
+        prog, exp, 5, ["--robot-name=g1"], extra_env={"RETRY_MAX_ATTEMPTS": "2"}
     )
     # A crash that advanced the checkpoint is treated as transient, and the next
     # run completes, so the job succeeds. (This case exercises the progress path
@@ -257,11 +257,11 @@ def test_crash_after_progress_then_success(tmp_path):
 
 
 def test_progress_crash_resets_consecutive_no_progress_counter(tmp_path):
-    # The give-up rule is "NO_PROGRESS_LIMIT runs IN A ROW with no checkpoint
+    # The give-up rule is "RETRY_MAX_ATTEMPTS runs IN A ROW with no checkpoint
     # progress". A progressing crash must RESET that consecutive count -- otherwise
     # interleaved transient failures around real progress would be miscounted as a
     # deterministic failure and the job would give up while it is actually
-    # advancing. With NO_PROGRESS_LIMIT=2, this fake python never produces two
+    # advancing. With RETRY_MAX_ATTEMPTS=2, this fake python never produces two
     # no-progress crashes in a row (no-progress, progress, no-progress, progress,
     # success), so the run must succeed. Deleting the reset makes it give up at the
     # third run -- so this test, unlike the one above, actually depends on it.
@@ -282,7 +282,7 @@ def test_progress_crash_resets_consecutive_no_progress_counter(tmp_path):
     exp = tmp_path / "exp"
     exp.mkdir()
     r = _run_loop(
-        prog, exp, 9, ["--robot-name=g1"], extra_env={"NO_PROGRESS_LIMIT": "2"}
+        prog, exp, 9, ["--robot-name=g1"], extra_env={"RETRY_MAX_ATTEMPTS": "2"}
     )
     assert r.returncode == 0, r.stdout + r.stderr
     assert not (exp / ".lepton_loop_gave_up").exists(), "gave up despite never 2-in-a-row"
diff --git a/protomotions/tests/test_mdp_component_metadata_forwarding.py b/protomotions/tests/test_mdp_component_metadata_forwarding.py
new file mode 100644
index 000000000..49da97b6b
--- /dev/null
+++ b/protomotions/tests/test_mdp_component_metadata_forwarding.py
@@ -0,0 +1,302 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Tests for which metadata keys in static_params reach a compute function.
+
+Metadata keys such as ``threshold`` and ``weight`` are read by the combining
+logic in ``base_env/utils.py``. A metadata key is also passed to the compute
+function when, and only when, that function declares a parameter of the same
+name. Termination kernels declare ``threshold``, so a termination's configured
+threshold must reach its kernel. Evaluation kernels do not, because for them
+``threshold`` is the failure cut that ``combine_evaluation`` applies.
+"""
+
+import inspect
+import pickle
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+import protomotions.envs.component_manager as component_manager
+from protomotions.envs import component_factories as factories
+from protomotions.envs import obs as obs_kernels
+from protomotions.envs import rewards as reward_kernels
+from protomotions.envs import terminations as termination_kernels
+from protomotions.envs.base_env.utils import (
+    combine_evaluation,
+    combine_rewards,
+    combine_terminations,
+)
+from protomotions.envs.component_manager import ComponentManager
+from protomotions.envs.mdp_component import _METADATA_KEYS, MdpComponent
+from protomotions.envs.terminations.task import check_path_distance_term
+
+TERMINATION_FACTORIES = [
+    factories.tracking_error_term_factory,
+    factories.anchor_pos_error_term_factory,
+    factories.anchor_ori_error_term_factory,
+    factories.relative_body_pos_error_term_factory,
+    factories.anchor_height_error_term_factory,
+]
+
+EVALUATION_FACTORIES = [
+    factories.gt_error_factory,
+    factories.max_joint_error_factory,
+    factories.gr_error_factory,
+    factories.anchor_pos_metric_factory,
+    factories.anchor_ori_metric_factory,
+    factories.relative_body_pos_metric_factory,
+    factories.anchor_height_error_metric_factory,
+]
+
+
+class _AnyContext:
+    """Context stand-in where every attribute path resolves to this object.
+
+    ``resolve_args`` only walks the binding paths and filters static_params, so
+    this is enough to see which parameters a compute function would receive.
+    """
+
+    def __getattr__(self, name):
+        return self
+
+
+def _func_params(component: MdpComponent) -> dict:
+    _, func_params = component.resolve_args(_AnyContext())
+    return func_params
+
+
+def _body_pos_context(max_body_errors):
+    """Context with one body per env offset along x by the given distance."""
+    errors = torch.tensor(max_body_errors)
+    ref = torch.zeros(len(errors), 2, 3)
+    current = ref.clone()
+    current[:, 1, 0] = errors
+    return SimpleNamespace(
+        current=SimpleNamespace(rigid_body_pos=current),
+        mimic=SimpleNamespace(ref_state=SimpleNamespace(rigid_body_pos=ref)),
+    )
+
+
+@pytest.fixture
+def eager_manager(monkeypatch):
+    monkeypatch.setattr(component_manager, "TORCH_COMPILE_AVAILABLE", False)
+    return ComponentManager(torch.device("cpu"))
+
+
+# ---------- terminations ------------------------------------------------------
+
+
+@pytest.mark.parametrize("factory", TERMINATION_FACTORIES, ids=lambda f: f.__name__)
+def test_termination_factories_pass_configured_threshold_to_kernel(factory):
+    component = factory(threshold=0.123)
+
+    assert _func_params(component) == {"threshold": 0.123}
+
+
+@pytest.mark.parametrize("factory", TERMINATION_FACTORIES, ids=lambda f: f.__name__)
+def test_termination_factory_default_matches_kernel_default(factory):
+    # If these differed, every config relying on the factory default would
+    # change behaviour now that the threshold reaches the kernel.
+    component = factory()
+    kernel_default = (
+        inspect.signature(component.compute_func).parameters["threshold"].default
+    )
+
+    assert component.get_params()["threshold"] == kernel_default
+
+
+def test_tracking_error_termination_uses_configured_threshold_in_step_path(
+    eager_manager,
+):
+    # 0.55 m is above the kernel default of 0.5 m but below the configured 0.6 m.
+    components = {"tracking_error": factories.tracking_error_term_factory(0.6)}
+    ctx = _body_pos_context([0.55, 0.65])
+
+    raw_terms = eager_manager.execute_all(components, ctx)
+    reset_buf, terminate_buf, logs = combine_terminations(
+        raw_terms, components, num_envs=2, device=torch.device("cpu")
+    )
+
+    assert torch.equal(reset_buf, torch.tensor([False, True]))
+    assert torch.equal(terminate_buf, torch.tensor([False, True]))
+    assert torch.equal(logs["termination/tracking_error"], torch.tensor([0.0, 1.0]))
+
+    single = eager_manager.execute_single(
+        "tracking_error", components["tracking_error"], ctx
+    )
+    assert torch.equal(single, torch.tensor([False, True]))
+
+
+# ---------- evaluation --------------------------------------------------------
+
+
+@pytest.mark.parametrize("factory", EVALUATION_FACTORIES, ids=lambda f: f.__name__)
+def test_evaluation_factories_keep_threshold_out_of_kernel(factory):
+    component = factory(threshold=0.123)
+
+    assert "threshold" not in _func_params(component)
+    assert component.get_params()["threshold"] == 0.123
+    assert "threshold" not in inspect.signature(component.compute_func).parameters
+
+
+def test_boolean_evaluation_wrappers_do_not_receive_threshold_through_kwargs():
+    # These wrappers forward **kwargs into TorchScript kernels that would reject
+    # an unexpected ``threshold`` argument.
+    path_metric = factories.path_distance_error_factory(threshold=1.5, min_progress=3)
+    steering_metric = factories.steering_velocity_error_factory(
+        speed_tolerance=0.4, direction_tolerance=0.6
+    )
+
+    assert _func_params(path_metric) == {"fail_dist": 1.5, "min_progress": 3}
+    assert _func_params(steering_metric) == {
+        "speed_tolerance": 0.4,
+        "direction_tolerance": 0.6,
+    }
+
+
+def test_evaluation_failure_cut_is_applied_to_kernel_output(eager_manager):
+    components = {"gt_error": factories.gt_error_factory(threshold=0.3)}
+    # Two bodies, one of them offset, so the mean error is half the offset.
+    ctx = _body_pos_context([0.4, 0.8])
+
+    raw_values = eager_manager.execute_all(components, ctx)
+    failed_buf, values, failures = combine_evaluation(
+        raw_values, components, num_envs=2, device=torch.device("cpu")
+    )
+
+    assert torch.allclose(values["gt_error"], torch.tensor([0.2, 0.4]))
+    assert torch.equal(failures["gt_error"], torch.tensor([False, True]))
+    assert torch.equal(failed_buf, torch.tensor([False, True]))
+
+
+# ---------- rewards -----------------------------------------------------------
+
+
+def test_reward_metadata_stays_out_of_reward_kernels():
+    components = {
+        "gt_rew": factories.gt_rew_factory(weight=0.5, coefficient=-100.0),
+        "power": factories.pow_rew_factory(weight=-1e-5, min_value=-0.5),
+        "contact_match": factories.contact_match_rew_factory(
+            weight=-0.1, zero_during_grace_period=True
+        ),
+    }
+
+    assert _func_params(components["gt_rew"]) == {"coefficient": -100.0}
+    assert _func_params(components["power"]) == {"use_torque_squared": False}
+    assert _func_params(components["contact_match"]) == {}
+
+    reward, logs = combine_rewards(
+        raw_rewards={
+            "gt_rew": torch.tensor([1.0, 2.0]),
+            "power": torch.tensor([1e5, 1e4]),
+            "contact_match": torch.tensor([3.0, 4.0]),
+        },
+        configs=components,
+        grace_mask=torch.tensor([True, False]),
+        num_envs=2,
+        device=torch.device("cpu"),
+    )
+
+    assert torch.allclose(logs["scaled_r/power"], torch.tensor([-0.5, -0.1]))
+    assert torch.allclose(logs["scaled_r/contact_match"], torch.tensor([0.0, -0.4]))
+    assert torch.allclose(reward, torch.tensor([0.0, 0.5]))
+
+
+def test_contact_force_change_reward_receives_its_threshold():
+    # The kernel declares ``threshold`` (the force change it ignores), so it
+    # now receives the configured value. Its default matches the factory's.
+    component = factories.contact_force_change_rew_factory(
+        weight=-2e-4, min_value=-0.5, threshold=45.0
+    )
+    kernel_default = (
+        inspect.signature(component.compute_func).parameters["threshold"].default
+    )
+
+    assert _func_params(component) == {"threshold": 45.0}
+    assert factories.contact_force_change_rew_factory().get_params()["threshold"] == (
+        kernel_default
+    )
+
+
+def test_no_kernel_declares_a_reward_combining_metadata_key():
+    # A kernel that declared ``weight`` or a clamp bound would receive the value
+    # that combine_rewards also applies, so the reward would be scaled twice.
+    combining_keys = _METADATA_KEYS - {"threshold"}
+    offenders = []
+    for module in (termination_kernels, reward_kernels, obs_kernels):
+        for name in module.__all__:
+            func = getattr(module, name)
+            if not callable(func):
+                continue
+            try:
+                parameters = inspect.signature(func).parameters
+            except (TypeError, ValueError):
+                continue
+            offenders += [f"{name}({key})" for key in combining_keys & set(parameters)]
+
+    assert offenders == []
+
+
+# ---------- general forwarding rules ------------------------------------------
+
+
+def _value_and_threshold(value, threshold=0.5):
+    return value > threshold
+
+
+def _value_and_kwargs(value, **kwargs):
+    return value
+
+
+class _UninspectableKernel:
+    """Callable whose signature cannot be read, like a TorchScript function."""
+
+    __signature__ = "not a signature"
+
+    def __call__(self, value, threshold=0.5):
+        return value > threshold
+
+
+@pytest.mark.parametrize(
+    ("compute_func", "expected"),
+    [
+        (_value_and_threshold, {"scale": 2.0, "threshold": 0.7}),
+        (_value_and_kwargs, {"scale": 2.0}),
+        (_UninspectableKernel(), {"scale": 2.0}),
+        (check_path_distance_term, {"scale": 2.0}),
+    ],
+    ids=["declared", "kwargs-only", "uninspectable", "torchscript"],
+)
+def test_metadata_reaches_only_kernels_that_declare_it(compute_func, expected):
+    component = MdpComponent(
+        compute_func=compute_func,
+        dynamic_vars={},
+        static_params={"scale": 2.0, "threshold": 0.7, "weight": 3.0},
+    )
+
+    assert _func_params(component) == expected
+
+
+def test_metadata_forwarding_survives_pickle_round_trip():
+    component = factories.tracking_error_term_factory(threshold=0.6)
+    assert _func_params(component) == {"threshold": 0.6}
+
+    restored = pickle.loads(pickle.dumps(component))
+
+    assert _func_params(restored) == {"threshold": 0.6}
+
+
+def test_component_pickled_before_forwarding_existed_still_forwards():
+    component = factories.tracking_error_term_factory(threshold=0.6)
+    state = component.__getstate__()
+    # Pickles written by earlier code carry only these attributes.
+    state = {
+        key: state[key]
+        for key in ("compute_func", "dynamic_vars", "static_params", "compile")
+    }
+    restored = MdpComponent.__new__(MdpComponent)
+    restored.__setstate__(state)
+
+    assert _func_params(restored) == {"threshold": 0.6}
diff --git a/protomotions/tests/test_mimic_evaluator_helpers.py b/protomotions/tests/test_mimic_evaluator_helpers.py
index 573e651ac..2c406820b 100644
--- a/protomotions/tests/test_mimic_evaluator_helpers.py
+++ b/protomotions/tests/test_mimic_evaluator_helpers.py
@@ -10,6 +10,10 @@
 import torch
 from tensordict import TensorDict
 
+from protomotions.agents.base_agent.model import (
+    ProtoMotionsTensorDictModule,
+    RolloutStateSpec,
+)
 from protomotions.agents.evaluators.config import (
     MimicEvaluatorConfig,
     MotionWeightsRulesConfig,
@@ -40,14 +44,10 @@ def get_shape_mapping(self, flattened=True):
 class _Simulator:
     def __init__(self):
         self._push_enabled = True
-        self.parked = []
 
     def get_robot_state(self):
         return _RobotState()
 
-    def park_envs(self, env_ids):
-        self.parked.append(env_ids.clone())
-
 
 class _MotionLib:
     def __init__(self):
@@ -128,9 +128,39 @@ def step(self, actions):
         )
 
 
-class _Model:
-    def __call__(self, obs_td):
-        return {"mean_action": obs_td["obs"] + 1.0}
+class _Model(ProtoMotionsTensorDictModule):
+    def __init__(self):
+        super().__init__()
+        self.in_keys = ["obs"]
+        self.out_keys = ["mean_action"]
+
+    def forward(self, tensordict, log_internals=False):
+        tensordict["mean_action"] = tensordict["obs"] + 1.0
+        return tensordict
+
+
+class _StatefulModel(_Model):
+    """Adds a per-env value to the action; each reset seeds the next generation."""
+
+    def __init__(self):
+        super().__init__()
+        self.generation = 0
+
+    def _next_generation(self, num_envs, device):
+        self.generation += 1
+        return torch.full((num_envs, 1), float(self.generation), device=device)
+
+    def rollout_state_specs(self):
+        return {
+            "episode_value": RolloutStateSpec(shape=(1,), init=self._next_generation)
+        }
+
+    def forward(self, tensordict, log_internals=False):
+        self.read_rollout_state(tensordict)
+        tensordict["mean_action"] = (
+            tensordict["obs"] + 1.0 + tensordict["episode_value"]
+        )
+        return tensordict
 
 
 class _Agent:
@@ -288,17 +318,6 @@ def test_mimic_motion_sampling_weights_handles_no_failures_and_zero_failure_disc
     )
 
 
-def test_mimic_parks_inactive_envs_only_when_batch_is_partial(tmp_path):
-    evaluator = _evaluator(tmp_path, num_envs=4)
-
-    evaluator._park_inactive_envs(torch.tensor([1, 3]))
-    evaluator._park_inactive_envs(torch.arange(4))
-    evaluator._park_inactive_envs(None)
-
-    assert len(evaluator.env.simulator.parked) == 1
-    assert torch.equal(evaluator.env.simulator.parked[0], torch.tensor([0, 2]))
-
-
 def test_mimic_build_eval_batches_prefers_fixed_motions_then_chunks_all_motions(tmp_path):
     evaluator = _evaluator(tmp_path, num_envs=2)
     evaluator.motion_manager.fixed = (
@@ -405,8 +424,8 @@ def test_mimic_check_eval_components_skips_when_no_clip_is_active(tmp_path):
     assert checked == []
 
 
-def test_mimic_evaluate_episode_applies_action_ema_and_records_actions(tmp_path):
-    evaluator = _evaluator(tmp_path, eval_action_ema_alpha=0.5)
+def test_mimic_evaluate_episode_steps_and_records_the_policy_actions(tmp_path):
+    evaluator = _evaluator(tmp_path)
     evaluator._episode_ctx = MimicEpisodeContext(
         motion_ids=torch.tensor([0, 1]),
         frame_limits=torch.tensor([2, 2]),
@@ -416,9 +435,141 @@ def test_mimic_evaluate_episode_applies_action_ema_and_records_actions(tmp_path)
 
     evaluator.evaluate_episode(torch.tensor([0, 1]), max_steps=2)
 
+    # The fake policy acts obs + 1 and the fake env echoes the action back as
+    # the next obs, so the raw actions are 1 and then 2, applied unfiltered.
     assert torch.equal(evaluator.env.step_actions[0], torch.ones(2, 2))
-    assert torch.equal(evaluator.env.step_actions[1], torch.full((2, 2), 1.5))
-    assert torch.equal(evaluator._metrics["actions"].data[0, 1], torch.full((2,), 1.5))
+    assert torch.equal(evaluator.env.step_actions[1], torch.full((2, 2), 2.0))
+    assert torch.equal(evaluator._metrics["actions"].data[0, 1], torch.full((2,), 2.0))
+
+
+def test_mimic_unscored_envs_play_fixed_background_clips(tmp_path):
+    evaluator = _evaluator(tmp_path, num_envs=6)
+    # Envs 1 and 4 are scene-paired with clips 2 and 0; the others have no scene.
+    evaluator.motion_manager._fixed_motion_ids_per_env = torch.tensor(
+        [-1, 2, -1, -1, 0, -1]
+    )
+    evaluator._episode_ctx = MimicEpisodeContext(
+        motion_ids=torch.tensor([1]),
+        frame_limits=torch.tensor([2]),
+    )
+
+    evaluator._on_episode_start(torch.tensor([3]))
+
+    # Scene-paired envs keep their own clip, the rest cycle through the 3 library
+    # clips, and the scored env 3 gets this batch's clip. Everyone starts at frame 0.
+    assert torch.equal(
+        evaluator.motion_manager.motion_ids, torch.tensor([0, 2, 2, 1, 0, 2])
+    )
+    assert torch.equal(evaluator.motion_manager.motion_times, torch.zeros(6))
+
+
+def _scored_and_unscored_env_finish_on_first_step(evaluator):
+    """Score envs 0 and 1 of 4; scored env 0 and unscored env 2 finish at step 1."""
+    evaluator._episode_ctx = MimicEpisodeContext(
+        motion_ids=torch.tensor([0, 1]),
+        frame_limits=torch.tensor([2, 2]),
+    )
+    evaluator._check_evaluation_failures = (
+        lambda active_env_ids, active_motion_ids: None
+    )
+    env = evaluator.env
+    base_step = env.step
+
+    def step(actions):
+        obs, rewards, dones, terminated, extras = base_step(actions)
+        env.motion_manager.motion_times += 0.5
+        if len(env.step_actions) == 1:
+            dones = torch.tensor([True, False, True, False])
+        return obs, rewards, dones, terminated, extras
+
+    env.step = step
+    return torch.tensor([0, 1])
+
+
+def test_mimic_evaluate_episode_restarts_only_unscored_envs_that_finish(tmp_path):
+    evaluator = _evaluator(tmp_path, num_envs=4)
+    evaluator._metrics = {"actions": _metric(num_motions=3, features=2)}
+    env = evaluator.env
+    scored_ids = _scored_and_unscored_env_finish_on_first_step(evaluator)
+
+    evaluator.evaluate_episode(scored_ids, max_steps=2)
+
+    (start_ids, start_kwargs), (restart_ids, restart_kwargs) = env.reset_calls
+    assert torch.equal(start_ids, torch.arange(4))
+    # Only the unscored env restarts; a scored env keeps its result as it was.
+    assert torch.equal(restart_ids, torch.tensor([2]))
+    assert start_kwargs == restart_kwargs == evaluator._get_reset_kwargs()
+    # It replays its own background clip from frame 0.
+    assert torch.equal(env.motion_manager.motion_ids, torch.tensor([0, 1, 2, 0]))
+    assert torch.equal(
+        env.motion_manager.motion_times, torch.tensor([1.0, 1.0, 0.5, 1.0])
+    )
+
+
+def test_mimic_evaluate_episode_scores_each_step_before_restarting_envs(tmp_path):
+    # A restart discards the env's cached context, which the failure checks read
+    # for the scored envs too. Scoring after the restart would judge the step
+    # against rebuilt context instead of the state the policy just produced.
+    evaluator = _evaluator(tmp_path, num_envs=4)
+    evaluator._metrics = {"actions": _metric(num_motions=3, features=2)}
+    env = evaluator.env
+    scored_ids = _scored_and_unscored_env_finish_on_first_step(evaluator)
+    events = []
+    evaluator._check_evaluation_failures = (
+        lambda active_env_ids, active_motion_ids: events.append(
+            ("score", active_env_ids.tolist())
+        )
+    )
+    record_step = evaluator._on_episode_step
+
+    def on_episode_step(env_ids, extras, actions):
+        events.append(("record", env_ids.tolist()))
+        record_step(env_ids, extras, actions)
+
+    evaluator._on_episode_step = on_episode_step
+    base_reset = env.reset
+
+    def reset(env_ids=None, **kwargs):
+        events.append(("reset", env_ids.tolist()))
+        return base_reset(env_ids, **kwargs)
+
+    env.reset = reset
+
+    evaluator.evaluate_episode(scored_ids, max_steps=2)
+
+    assert events == [
+        ("reset", [0, 1, 2, 3]),
+        ("score", [0, 1]),
+        ("record", [0, 1]),
+        ("reset", [2]),
+        ("score", [0, 1]),
+        ("record", [0, 1]),
+    ]
+
+
+def test_mimic_evaluation_reseeds_model_context_only_for_envs_it_resets(tmp_path):
+    evaluator = _evaluator(tmp_path, num_envs=4)
+    model = evaluator.agent.model = _StatefulModel()
+    model.reset_rollout_context(num_envs=4, device=torch.device("cpu"))
+    training_context = model.episode_value.clone()
+    env = evaluator.env
+    scored_ids = _scored_and_unscored_env_finish_on_first_step(evaluator)
+
+    evaluator._metrics = evaluator.initialize_eval()
+    evaluator.evaluate_episode(scored_ids, max_steps=2)
+
+    # The episode start resets every env, so every env gets generation 2 in place
+    # of training's generation 1. The fake env returns zero observations, so each
+    # action is 1 plus the env's generation.
+    assert torch.equal(env.step_actions[0], torch.full((4, 2), 3.0))
+    # Only the restarted unscored env 2 gets generation 3. Scored env 0 also
+    # finished but is not restarted, so its context stays as it was.
+    assert torch.equal(env.step_actions[1][:, 0], torch.tensor([3.0, 3.0, 4.0, 3.0]))
+
+    evaluator.cleanup_after_evaluation()
+
+    # Training resumes with the context it had before evaluation.
+    assert torch.equal(model.episode_value, training_context)
 
 
 def test_mimic_process_eval_results_updates_weights_and_additional_metrics(tmp_path):
diff --git a/protomotions/tests/test_model_licenses.py b/protomotions/tests/test_model_licenses.py
new file mode 100644
index 000000000..94ee85c6e
--- /dev/null
+++ b/protomotions/tests/test_model_licenses.py
@@ -0,0 +1,115 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import json
+from pathlib import Path
+import runpy
+import shutil
+
+import pytest
+
+
+ROOT = Path(__file__).resolve().parents[2]
+POLICY = runpy.run_path(str(ROOT / "scripts/check_model_licenses.py"))
+MODEL = "data/pretrained_models/test/model"
+
+
+@pytest.fixture
+def release(tmp_path):
+    (tmp_path / "legal").mkdir()
+    shutil.copyfile(ROOT / "legal/NVIDIA-Open-Model-License.pdf",
+                    tmp_path / "legal/NVIDIA-Open-Model-License.pdf")
+    (tmp_path / "legal/model-license-exceptions.json").write_text("{}")
+    model = tmp_path / MODEL
+    model.mkdir(parents=True)
+    shutil.copyfile(tmp_path / "legal/NVIDIA-Open-Model-License.pdf", model / "LICENSE.pdf")
+    (model / "NOTICE.txt").write_text(POLICY["NOTICE"])
+    (model / "last.ckpt").write_text("LFS pointer; no payload needed")
+    (model / "MODEL_CARD.md").write_text(
+        "# Test model\n\n## License\n\nModel license: NVIDIA Open Model License\n\n"
+        "[License](LICENSE.pdf) and [Notice](NOTICE.txt)\n")
+    return tmp_path
+
+
+def test_published_models_comply():
+    assert POLICY["check"](ROOT) == []
+
+
+def test_complete_bundle_passes(release):
+    assert POLICY["check"](release) == []
+
+
+@pytest.mark.parametrize("filename", ["MODEL_CARD.md", "LICENSE.pdf", "NOTICE.txt"])
+def test_missing_required_file_fails(release, filename):
+    (release / MODEL / filename).unlink()
+    assert POLICY["check"](release)
+
+
+@pytest.mark.parametrize("filename", ["LICENSE.pdf", "NOTICE.txt"])
+def test_altered_license_or_notice_fails(release, filename):
+    (release / MODEL / filename).write_text("Apache-2.0")
+    assert POLICY["check"](release)
+
+
+@pytest.mark.parametrize("suffix", [".ckpt", ".onnx", ".safetensors", ".pth", ".pt", ".engine"])
+def test_uncarded_release_is_not_skipped(release, suffix):
+    orphan = release / "data/pretrained_models/new/export_only"
+    orphan.mkdir(parents=True)
+    (orphan / ("weights" + suffix)).write_text("pointer")
+    assert any("new/export_only" in e for e in POLICY["check"](release))
+
+
+def test_nested_exports_use_enclosing_card(release):
+    export = release / MODEL / "compiled_models"
+    export.mkdir()
+    (export / "policy.onnx").write_text("pointer")
+    assert POLICY["check"](release) == []
+
+
+def test_explicit_exception_requires_matching_card_and_license(release):
+    entry = {"license": "Example third-party license", "license_file": "LICENSE.txt",
+             "reason": "Upstream model has separate terms"}
+    (release / "legal/model-license-exceptions.json").write_text(json.dumps({MODEL: entry}))
+    assert POLICY["check"](release)
+    (release / MODEL / "MODEL_CARD.md").write_text(
+        "## License\nModel license: Example third-party license\n"
+        "License exception: Upstream model has separate terms\n"
+        "[License](LICENSE.txt) and [Notice](NOTICE.txt)\n")
+    (release / MODEL / "LICENSE.txt").write_text("Example full license")
+    (release / MODEL / "NOTICE.txt").write_text("Example upstream attribution")
+    assert POLICY["check"](release) == []
+
+
+@pytest.mark.parametrize("entry", [
+    {}, {"license": "Other", "reason": "", "license_file": "LICENSE.txt"},
+    {"license": "Other", "reason": "Upstream", "license_file": "../LICENSE.txt"},
+    {"license": "Other", "reason": "Upstream", "license_file": "MODEL_CARD.md"},
+])
+def test_malformed_exception_fails(release, entry):
+    (release / "legal/model-license-exceptions.json").write_text(json.dumps({MODEL: entry}))
+    assert POLICY["check"](release)
+
+
+def test_stale_exception_fails(release):
+    (release / "legal/model-license-exceptions.json").write_text('{"removed/model": {}}')
+    assert any("Stale" in e for e in POLICY["check"](release))
+
+
+def test_canonical_license_cannot_drift(release):
+    (release / "legal/NVIDIA-Open-Model-License.pdf").write_text("Replacement")
+    assert any("canonical" in e for e in POLICY["check"](release))
+
+
+def test_code_license_stays_apache():
+    assert "Apache License" in (ROOT / "LICENSE.md").read_text()
+    assert "SPDX-License-Identifier: Apache-2.0" in (ROOT / ".license-header.txt").read_text()
+
+
+def test_public_ci_checks_model_licenses_without_lfs():
+    import yaml
+
+    workflow = yaml.safe_load((ROOT / ".github/workflows/public-ci.yml").read_text())
+    steps = workflow["jobs"]["model-licenses"]["steps"]
+    assert any(step.get("run") == "python scripts/check_model_licenses.py" for step in steps)
+    checkout = next(step for step in steps if step.get("uses", "").startswith("actions/checkout@"))
+    assert checkout["with"]["lfs"] is False
diff --git a/protomotions/tests/test_mujoco_actuators_and_reset.py b/protomotions/tests/test_mujoco_actuators_and_reset.py
new file mode 100644
index 000000000..182e47e88
--- /dev/null
+++ b/protomotions/tests/test_mujoco_actuators_and_reset.py
@@ -0,0 +1,266 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""MuJoCo backend: actuator gear, integrator choice and root quaternion order.
+
+SMPL, SMPL-X, SOMA23 and AMP declare their motors with a gear (50-500). MuJoCo
+applies gear * actuator force to the joint, while ProtoMotions writes joint
+torques and PD targets straight into ctrl, so the backend must reset the gear
+to 1. These tests build the real simulator from the shipped assets.
+"""
+
+import numpy as np
+import pytest
+import torch
+
+mujoco = pytest.importorskip("mujoco")
+
+from protomotions.components.scene_lib import SceneLib  # noqa: E402
+from protomotions.robot_configs.base import ControlType  # noqa: E402
+from protomotions.robot_configs.factory import robot_config  # noqa: E402
+from protomotions.simulator.base_simulator.simulator_state import (  # noqa: E402
+    ResetState,
+    StateConversion,
+)
+from protomotions.simulator.mujoco.config import MujocoSimulatorConfig  # noqa: E402
+from protomotions.simulator.mujoco.simulator import MujocoSimulator  # noqa: E402
+
+GEARED_ROBOTS = ["smpl", "soma23"]
+
+
+def _build(rc, config):
+    sim = MujocoSimulator(
+        config=config,
+        robot_config=rc,
+        terrain=None,
+        device=torch.device("cpu"),
+        scene_lib=SceneLib.empty(num_envs=1, device="cpu", terrain=None),
+    )
+    sim._initialize_with_markers({})
+    return sim
+
+
+def _make_sim(robot, control_type=None, use_implicit_pd=True, integrator="keep"):
+    rc = robot_config(robot)
+    if control_type is not None:
+        rc.control.control_type = control_type
+    sim_params = rc.simulation_params.mujoco
+    if integrator != "keep":
+        sim_params.integrator = integrator
+    config = MujocoSimulatorConfig(
+        num_envs=1,
+        headless=True,
+        experiment_name="test_mujoco_actuators",
+        sim=sim_params,
+        use_implicit_pd=use_implicit_pd,
+    )
+    return _build(rc, config)
+
+
+def _actuated_kp(sim):
+    """Stiffness of each actuated DOF in qpos order, read from the robot config."""
+    n = sim._num_actuated_dofs
+    names = [
+        mujoco.mj_id2name(
+            sim.model, mujoco.mjtObj.mjOBJ_JOINT, sim.model.dof_jntid[6 + i]
+        )
+        for i in range(n)
+    ]
+    info = sim.robot_config.control.control_info
+    return np.array([info[name].stiffness for name in names])
+
+
+def _set_dof_pos(sim, dof_pos):
+    n = sim._num_actuated_dofs
+    sim.data.qpos[7 : 7 + n] = dof_pos
+    sim.data.qvel[:] = 0.0
+    mujoco.mj_forward(sim.model, sim.data)
+
+
+def _random_targets(n, seed=0):
+    # float32 round trip so qpos holds exactly what ctrl received
+    rng = np.random.default_rng(seed)
+    return rng.uniform(-0.3, 0.3, n).astype(np.float32).astype(np.float64)
+
+
+@pytest.mark.parametrize("robot", GEARED_ROBOTS)
+def test_position_actuators_hold_the_target_itself(robot):
+    sim = _make_sim(robot)
+    n = sim._num_actuated_dofs
+    assert np.all(sim.model.actuator_gear[:, 0] == 1.0)
+
+    target = _random_targets(n)
+    sim._apply_simulator_pd_targets(torch.tensor(target, dtype=torch.float32)[None])
+
+    # Before the fix this was gear * effort (up to 250,000 N*m), pushing away.
+    _set_dof_pos(sim, target)
+    np.testing.assert_allclose(sim.data.qfrc_actuator[6 : 6 + n], 0.0, atol=1e-3)
+
+    delta = 0.01
+    _set_dof_pos(sim, target + delta)
+    np.testing.assert_allclose(
+        sim.data.qfrc_actuator[6 : 6 + n], -_actuated_kp(sim) * delta, rtol=1e-4
+    )
+
+
+@pytest.mark.parametrize("robot", GEARED_ROBOTS)
+def test_torque_mode_applies_the_commanded_joint_torque(robot):
+    sim = _make_sim(robot, control_type=ControlType.TORQUE)
+    n = sim._num_actuated_dofs
+
+    torques = np.linspace(-5.0, 5.0, n).astype(np.float32)
+    sim._apply_simulator_torques(torch.tensor(torques)[None])
+    mujoco.mj_forward(sim.model, sim.data)
+
+    np.testing.assert_allclose(sim.data.qfrc_actuator[6 : 6 + n], torques, atol=1e-5)
+
+
+@pytest.mark.parametrize("robot", GEARED_ROBOTS)
+def test_explicit_pd_applies_the_torque_it_reports(robot):
+    sim = _make_sim(robot, use_implicit_pd=False)
+    n = sim._num_actuated_dofs
+
+    target = _random_targets(n)
+    delta = 0.01
+    _set_dof_pos(sim, target + delta)
+    sim._apply_simulator_pd_targets(torch.tensor(target, dtype=torch.float32)[None])
+    mujoco.mj_forward(sim.model, sim.data)
+
+    applied = sim.data.qfrc_actuator[6 : 6 + n]
+    np.testing.assert_allclose(applied, sim._last_applied_torques, rtol=1e-5, atol=1e-5)
+    np.testing.assert_allclose(applied, -_actuated_kp(sim) * delta, rtol=1e-4)
+
+
+def test_gear_reset_keeps_declared_limits_in_joint_units():
+    xml = """
+    <mujoco>
+      <worldbody>
+        <body>
+          <joint name="j" type="hinge" axis="1 0 0"/>
+          <geom size="0.1"/>
+        </body>
+      </worldbody>
+      <actuator>
+        <motor joint="j" gear="50" ctrllimited="true" ctrlrange="-1 1"
+               forcelimited="true" forcerange="-2 2"/>
+      </actuator>
+    </mujoco>
+    """
+    model = mujoco.MjModel.from_xml_string(xml)
+    data = mujoco.MjData(model)
+
+    def joint_torque(ctrl):
+        data.ctrl[0] = ctrl
+        mujoco.mj_forward(model, data)
+        return data.qfrc_actuator[0]
+
+    commands = [0.5, 1.0, 3.0, -4.0]
+    before = [joint_torque(c) for c in commands]
+
+    sim = object.__new__(MujocoSimulator)
+    sim.model = model
+    sim._neutralize_actuator_gear()
+
+    assert model.actuator_gear[0, 0] == 1.0
+    # ctrl is now the joint torque; the MJCF's +-50 N*m limit survives
+    after = [joint_torque(50.0 * c) for c in commands]
+    np.testing.assert_allclose(after, before)
+    assert max(abs(t) for t in after) == pytest.approx(50.0)
+
+
+def _gear_reset_on(joint_type, gear):
+    """Run the gear reset on a single motor driving one joint of `joint_type`."""
+    xml = f"""
+    <mujoco>
+      <worldbody>
+        <body>
+          <joint name="j" type="{joint_type}"/>
+          <geom size="0.1"/>
+        </body>
+      </worldbody>
+      <actuator>
+        <motor name="m" joint="j" gear="{gear}"/>
+      </actuator>
+    </mujoco>
+    """
+    sim = object.__new__(MujocoSimulator)
+    sim.model = mujoco.MjModel.from_xml_string(xml)
+    sim._neutralize_actuator_gear()
+    return sim.model
+
+
+@pytest.mark.parametrize("joint_type", ["hinge", "slide"])
+def test_gear_reset_accepts_a_positive_gear_on_a_one_dof_joint(joint_type):
+    model = _gear_reset_on(joint_type, "50")
+    np.testing.assert_array_equal(model.actuator_gear[0], [1, 0, 0, 0, 0, 0])
+
+
+@pytest.mark.parametrize("gear", ["0", "-50"])
+def test_gear_reset_rejects_a_zero_or_negative_gear(gear):
+    with pytest.raises(ValueError, match="needs a positive gear"):
+        _gear_reset_on("hinge", gear)
+
+
+def test_gear_reset_rejects_a_ball_joint_actuator():
+    # A ball joint's gear is a 3D torque axis; collapsing it to a scalar 1
+    # would silently drive the joint about the wrong axis.
+    with pytest.raises(ValueError, match="hinge and slide joints only"):
+        _gear_reset_on("ball", "0 1 0 0 0 0")
+
+
+def test_reset_after_switching_from_isaaclab_writes_wxyz():
+    from protomotions.simulator.factory import update_simulator_config_for_test
+    from protomotions.simulator.isaaclab.config import IsaacLabSimulatorConfig
+
+    rc = robot_config("soma23")
+    config = IsaacLabSimulatorConfig(
+        num_envs=1,
+        headless=True,
+        experiment_name="test_mujoco_actuators",
+        sim=rc.simulation_params.isaaclab,
+    )
+    assert config.w_last is True
+    config = update_simulator_config_for_test(config, "mujoco", rc)
+    assert config.w_last is False
+    sim = _build(rc, config)
+    n = sim._num_actuated_dofs
+
+    # Turned ~145 degrees, mostly about the vertical axis
+    root_rot = torch.tensor([[0.1, -0.2, 0.9, 0.3]])
+    root_rot = root_rot / root_rot.norm(dim=-1, keepdim=True)
+    state = ResetState(
+        root_pos=torch.tensor([[0.0, 0.0, 1.0]]),
+        root_rot=root_rot.clone(),
+        root_vel=torch.zeros(1, 3),
+        root_ang_vel=torch.zeros(1, 3),
+        dof_pos=torch.zeros(1, n),
+        dof_vel=torch.zeros(1, n),
+        state_conversion=StateConversion.COMMON,
+    )
+    sim.reset_envs(state)
+
+    np.testing.assert_allclose(
+        sim.data.qpos[3:7], root_rot[0, [3, 0, 1, 2]].numpy(), atol=1e-6
+    )
+    read_back = sim.get_robot_state().root_rot[0]
+    assert torch.dot(read_back, root_rot[0]).abs().item() == pytest.approx(
+        1.0, abs=1e-6
+    )
+
+
+@pytest.mark.parametrize(
+    "integrator, expected",
+    [
+        ("keep", mujoco.mjtIntegrator.mjINT_IMPLICITFAST),  # smpl's robot config
+        (None, mujoco.mjtIntegrator.mjINT_EULER),  # MJCF / MuJoCo default
+        ("rk4", mujoco.mjtIntegrator.mjINT_RK4),
+    ],
+)
+def test_integrator_comes_from_sim_params(integrator, expected):
+    sim = _make_sim("smpl", integrator=integrator)
+    assert sim.model.opt.integrator == expected
+
+
+def test_unknown_integrator_is_rejected():
+    with pytest.raises(ValueError, match="Unknown MuJoCo integrator"):
+        _make_sim("smpl", integrator="verlet")
diff --git a/protomotions/tests/test_mujoco_body_velocity.py b/protomotions/tests/test_mujoco_body_velocity.py
new file mode 100644
index 000000000..e658d3af5
--- /dev/null
+++ b/protomotions/tests/test_mujoco_body_velocity.py
@@ -0,0 +1,89 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Body velocities must match derivatives of the returned world-frame poses."""
+
+import mujoco
+import numpy as np
+import pytest
+import torch
+
+from protomotions.simulator.base_simulator.simulator_state import StateConversion
+from protomotions.simulator.mujoco.simulator import MujocoSimulator
+
+
+XML = """
+<mujoco>
+  <worldbody>
+    <body name="root" pos="0 0 1">
+      <freejoint/>
+      <geom type="box" size=".1 .2 .1" pos=".07 0 .03" mass="5"/>
+      <body name="left_foot" pos="0 .2 -.4">
+        <joint name="left_hip" axis="0 1 0" pos="0 0 .3"/>
+        <geom type="box" size=".1 .05 .05" pos=".04 0 -.02" mass="1"/>
+      </body>
+      <body name="right_foot" pos="0 -.2 -.4">
+        <joint name="right_hip" axis="0 1 0" pos="0 0 .3"/>
+        <geom type="box" size=".1 .05 .05" pos=".04 0 -.02" mass="1"/>
+      </body>
+    </body>
+    <body name="scene_object" pos="2 0 1">
+      <freejoint/>
+      <geom type="sphere" size=".1" mass="1"/>
+    </body>
+  </worldbody>
+</mujoco>
+"""
+
+
+@pytest.mark.parametrize("motion", ["still", "translation", "rotation", "left", "right", "combined"])
+def test_body_velocities_match_pose_derivatives(motion):
+    model = mujoco.MjModel.from_xml_string(XML)
+    data = mujoco.MjData(model)
+    # A rotated base distinguishes world-frame from local-frame velocities.
+    data.qpos[3:7] = [np.cos(.4), 0, 0, np.sin(.4)]
+    data.qpos[7:9] = [.3, -.2]
+    if motion in {"translation", "combined"}:
+        data.qvel[:3] = [.7, -.2, .1]
+    if motion in {"rotation", "combined"}:
+        data.qvel[3:6] = [.4, -.3, .8]
+    if motion in {"left", "combined"}:
+        data.qvel[6] = 1.2
+    if motion in {"right", "combined"}:
+        data.qvel[7] = -.9
+    mujoco.mj_forward(model, data)
+
+    simulator = object.__new__(MujocoSimulator)
+    simulator.model = model
+    simulator.data = data
+    simulator._num_robot_bodies = 3
+    state = simulator._get_simulator_bodies_state()
+
+    epsilon = 1e-7
+    future = mujoco.MjData(model)
+    future.qpos[:] = data.qpos
+    mujoco.mj_integratePos(model, future.qpos, data.qvel, epsilon)
+    mujoco.mj_forward(model, future)
+    expected_linear = (future.xpos[1:4] - data.xpos[1:4]) / epsilon
+    rotation = data.xmat[1:4].reshape(-1, 3, 3)
+    next_rotation = future.xmat[1:4].reshape(-1, 3, 3)
+    delta = next_rotation @ rotation.transpose(0, 2, 1)
+    expected_angular = np.stack(
+        [delta[:, 2, 1] - delta[:, 1, 2],
+         delta[:, 0, 2] - delta[:, 2, 0],
+         delta[:, 1, 0] - delta[:, 0, 1]], axis=-1
+    ) / (2 * epsilon)
+
+    assert state.rigid_body_vel.shape == (1, 3, 3)  # No world/scene bodies.
+    assert state.rigid_body_vel.dtype == torch.float32
+    assert state.state_conversion == StateConversion.SIMULATOR
+    np.testing.assert_allclose(state.rigid_body_pos[0], data.xpos[1:4], atol=1e-7)
+    np.testing.assert_allclose(state.rigid_body_rot[0], data.xquat[1:4], atol=1e-7)
+    np.testing.assert_allclose(state.rigid_body_vel[0], expected_linear, atol=2e-6)
+    np.testing.assert_allclose(state.rigid_body_ang_vel[0], expected_angular, atol=2e-6)
+    if motion == "left":
+        assert np.linalg.norm(state.rigid_body_vel[0, 1]) > .1
+        np.testing.assert_array_equal(state.rigid_body_vel[0, 2], 0)
+    if motion == "right":
+        assert np.linalg.norm(state.rigid_body_vel[0, 2]) > .1
+        np.testing.assert_array_equal(state.rigid_body_vel[0, 1], 0)
diff --git a/protomotions/tests/test_mujoco_keyboard.py b/protomotions/tests/test_mujoco_keyboard.py
index 702f1a026..77b5e8b34 100644
--- a/protomotions/tests/test_mujoco_keyboard.py
+++ b/protomotions/tests/test_mujoco_keyboard.py
@@ -1,87 +1,200 @@
 # SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
+from contextlib import nullcontext
 from types import SimpleNamespace
-import queue
+from unittest.mock import Mock
 
+import numpy as np
 import pytest
 import torch
 
-from protomotions.simulator.mujoco.keyboard import (
-    MujocoKeyboardWindow,
-    _printable_key,
-)
+from protomotions.simulator.base_simulator.user_interface import UserInterface
+from protomotions.simulator.base_simulator.simulator import Simulator
+from protomotions.simulator.mujoco.keyboard import MujocoViewerKeyboard
 from protomotions.simulator.mujoco.simulator import MujocoSimulator
 
 
-def test_mujoco_rejects_body_mass_randomization_before_initialization():
-    config = SimpleNamespace(
-        domain_randomization=SimpleNamespace(body_mass=object()),
-    )
+class FakeViewer:
+    """Model MuJoCo's change-detected, bidirectional render-flag sync."""
 
-    with pytest.raises(
-        NotImplementedError,
-        match="MuJoCo does not support body-mass domain randomization",
-    ):
-        MujocoSimulator(
-            config=config,
-            robot_config=None,
-            terrain=None,
-            device=torch.device("cpu"),
-            scene_lib=None,
-        )
+    def __init__(self):
+        self.opt = SimpleNamespace(flags=np.zeros(4), geomgroup=np.ones(6))
+        self.user_scn = SimpleNamespace(flags=np.zeros(3))
+        self.native_flags = self.user_scn.flags.copy()
+        self.previous_flags = self.user_scn.flags.copy()
+        self.cam = SimpleNamespace()
+        self.sync_count = 0
 
+    def lock(self):
+        return nullcontext()
 
-def _window_with_events(*events):
-    window = object.__new__(MujocoKeyboardWindow)
-    window._events = queue.Queue()
-    window._process = SimpleNamespace(is_alive=lambda: True)
-    for event in events:
-        window._events.put(event)
-    return window
+    def sync(self):
+        changed = self.user_scn.flags != self.previous_flags
+        self.native_flags[changed] = self.user_scn.flags[changed]
+        self.user_scn.flags[:] = self.native_flags
+        self.previous_flags[:] = self.native_flags
+        self.sync_count += 1
 
 
-def test_keyboard_window_drains_key_transitions_without_gui():
-    window = _window_with_events(("w", True), ("w", False))
-
-    assert window.drain_events() == [("w", True), ("w", False)]
-    assert window.drain_events() == []
+def simulator_shell():
+    simulator = object.__new__(MujocoSimulator)
+    simulator.user_interface = UserInterface()
+    simulator._viewer_keyboard = MujocoViewerKeyboard()
+    return simulator
 
 
-@pytest.mark.parametrize(
-    "char, keysym, expected",
-    [("z", "z", "z"), ("", "Z", "z"), ("", "Shift_L", "")],
-)
-def test_keyboard_window_uses_keysym_when_tk_release_has_no_char(
-    char, keysym, expected
-):
-    assert _printable_key(char, keysym) == expected
+def test_mujoco_rejects_body_mass_randomization_before_initialization():
+    config = SimpleNamespace(domain_randomization=SimpleNamespace(body_mass=object()))
+    with pytest.raises(NotImplementedError, match="body-mass domain randomization"):
+        MujocoSimulator(config, None, None, torch.device("cpu"), None)
+
+
+def test_viewer_launch_wires_native_callback_without_second_window(monkeypatch):
+    import mujoco.viewer
+
+    simulator = simulator_shell()
+    simulator.model, simulator.data = object(), object()
+    viewer = FakeViewer()
+    launch = Mock(return_value=viewer)
+    monkeypatch.setattr(mujoco.viewer, "launch_passive", launch)
+    monkeypatch.setattr("atexit.register", lambda callback: None)
+    simulator._init_viewer()
+    assert launch.call_args.kwargs["key_callback"] == simulator._viewer_keyboard.on_key
+    assert not hasattr(simulator, "_keyboard_window")
+    calls = []
+    simulator.user_interface.register_key(
+        "R", owner="test", description="Reset", on_press=lambda: calls.append("reset")
+    )
+    launch.call_args.kwargs["key_callback"](ord("R"))
+    assert calls == []  # Viewer thread never runs task callbacks.
+    simulator._poll_keyboard_events()
+    assert calls == ["reset"]
 
 
-def test_simulator_forwards_keyboard_events_to_user_interface():
-    simulator = object.__new__(MujocoSimulator)
-    simulator._keyboard_window = SimpleNamespace(
-        drain_events=lambda: [("w", True), ("w", False)]
-    )
-    forwarded = []
-    simulator.user_interface = SimpleNamespace(
-        handle_key_event=lambda key, *, pressed: forwarded.append((key, pressed))
+def test_repeated_presses_use_existing_ui_queue():
+    simulator = simulator_shell()
+    key = simulator.user_interface.register_key(
+        "W", owner="test", description="Forward"
     )
-
+    for _ in range(3):
+        simulator._viewer_keyboard.on_key(ord("W"))
+    simulator.user_interface.begin_step()
     simulator._poll_keyboard_events()
+    for _ in range(3):
+        assert key.consume()
+        assert not key.consume()
+        simulator.user_interface.begin_step()
+    assert not key.consume()
+    assert not key.down()  # Passive viewer does not report releases / held state.
+
+
+def test_physics_step_dispatches_keys_before_control_and_integration(monkeypatch):
+    import mujoco
+    from protomotions.robot_configs.base import ControlType
+
+    simulator = simulator_shell()
+    simulator.config = SimpleNamespace(use_implicit_pd=True)
+    simulator.control_type = ControlType.TORQUE
+    simulator._max_action_latency_ms = 0.0
+    simulator._step_count = 0
+    simulator.decimation = 1
+    simulator.model, simulator.data = object(), object()
+    order = []
+    simulator._apply_control = lambda: order.append("control")
+    simulator._print_state_debug = lambda: None
+    monkeypatch.setattr(mujoco, "mj_step", lambda model, data: order.append("physics"))
+    simulator.user_interface.register_key(
+        "J",
+        owner="test",
+        description="Projectile",
+        on_press=lambda: order.append("key"),
+    )
+    simulator._viewer_keyboard.on_key(ord("J"))
+    simulator._physics_step()
+    assert order == ["key", "control", "physics"]
 
-    assert forwarded == [("w", True), ("w", False)]
 
+@pytest.mark.parametrize("keycode", [-1, 0, 32, 127, 256, 1000])
+def test_non_task_keys_are_left_to_native_viewer(keycode):
+    keyboard = MujocoViewerKeyboard()
+    keyboard.on_key(keycode)
+    assert list(keyboard.drain_presses()) == []
 
-@pytest.mark.parametrize(
-    "event, message",
-    [
-        (("__window_error__", "tk failed"), "tk failed"),
-        (("__window_closed__", False), "was closed"),
-    ],
-)
-def test_keyboard_window_reports_child_failures(event, message):
-    window = _window_with_events(event)
 
-    with pytest.raises(RuntimeError, match=message):
-        window.drain_events()
+def test_native_mode_suppresses_tasks_and_period_always_switches_back():
+    simulator = simulator_shell()
+    keyboard = simulator._viewer_keyboard
+    simulator.user_interface.register_key(
+        ".", owner="mujoco", description="Native mode", on_press=keyboard.toggle_native
+    )
+    calls = []
+    simulator.user_interface.register_key(
+        "R", owner="test", description="Reset", on_press=lambda: calls.append("reset")
+    )
+    for char in "R.R.R":
+        keyboard.on_key(ord(char))
+    simulator._poll_keyboard_events()
+    assert calls == ["reset", "reset"]
+    assert not keyboard.native
+
+
+def test_task_mode_restores_options_and_native_render_flags():
+    viewer = FakeViewer()
+    keyboard = MujocoViewerKeyboard()
+    keyboard.capture(viewer)
+    viewer.opt.flags[0] = 1  # N: island colouring
+    viewer.opt.geomgroup[0] = 0  # digits: hide geometries
+    viewer.native_flags[0] = 1  # W/R/L: private render scene changed
+    keyboard.sync(viewer)
+    np.testing.assert_array_equal(viewer.opt.flags, 0)
+    np.testing.assert_array_equal(viewer.opt.geomgroup, 1)
+    np.testing.assert_array_equal(viewer.native_flags, 0)
+    assert viewer.sync_count == 2
+    keyboard.sync(viewer)
+    assert viewer.sync_count == 3  # No extra sync in the steady state.
+
+
+def test_native_mode_leaves_visualization_changes_alone():
+    viewer = FakeViewer()
+    keyboard = MujocoViewerKeyboard()
+    keyboard.capture(viewer)
+    keyboard.toggle_native()
+    viewer.opt.flags[0] = 1
+    viewer.native_flags[0] = 1
+    keyboard.sync(viewer)
+    assert viewer.opt.flags[0] == viewer.native_flags[0] == 1
+    keyboard.toggle_native()
+    keyboard.sync(viewer)
+    assert viewer.opt.flags[0] == viewer.native_flags[0] == 0
+
+
+def test_render_uses_keyboard_sync_without_changing_mouse_orbit(monkeypatch):
+    simulator = simulator_shell()
+    simulator.headless = False
+    simulator._viewer_initialized = True
+    simulator.viewer = FakeViewer()
+    simulator.viewer.is_running = lambda: True
+    simulator.viewer.cam.distance = 4.0
+    simulator.viewer.cam.azimuth = 75.0
+    simulator.viewer.cam.elevation = -25.0
+    monkeypatch.setattr(Simulator, "render", lambda self: None)
+    simulator._viewer_keyboard.sync = Mock()
+    simulator.render()
+    simulator._viewer_keyboard.sync.assert_called_once_with(simulator.viewer)
+    assert simulator.viewer.cam.distance == 4.0
+    assert simulator.viewer.cam.azimuth == 75.0
+    assert simulator.viewer.cam.elevation == -25.0
+
+
+def test_headless_render_and_shutdown_do_not_need_a_viewer(monkeypatch):
+    simulator = simulator_shell()
+    simulator.headless = True
+    simulator._viewer_initialized = False
+    simulator.viewer = None
+    monkeypatch.setattr(Simulator, "render", lambda self: None)
+    simulator._viewer_keyboard.sync = Mock()
+    simulator.render()
+    simulator.shutdown()
+    simulator.shutdown()
+    simulator._viewer_keyboard.sync.assert_not_called()
diff --git a/protomotions/tests/test_mujoco_recording.py b/protomotions/tests/test_mujoco_recording.py
index 29a6b3300..a5b8aab72 100644
--- a/protomotions/tests/test_mujoco_recording.py
+++ b/protomotions/tests/test_mujoco_recording.py
@@ -9,21 +9,32 @@
 import matplotlib.pyplot as plt
 import mujoco
 import numpy as np
+import pytest
 
 from protomotions.simulator.mujoco.simulator import MujocoSimulator
 
 
-def test_viewport_capture_uses_live_viewer_camera(monkeypatch, tmp_path):
+@pytest.mark.parametrize("viewport_size", [None, (0, 0)])
+def test_viewport_capture_uses_live_viewer_camera(monkeypatch, tmp_path, viewport_size):
     calls = []
 
     class FakeRenderer:
-        def __init__(self, model, *, height, width):
+        def __init__(self, model, *, height, width, max_geom):
             assert (height, width) == (480, 640)
+            self.scene = mujoco.MjvScene(model, maxgeom=max_geom)
 
-        def update_scene(self, data, *, camera=-1):
-            calls.append((data, camera))
+        def update_scene(self, data, *, camera=-1, scene_option=None):
+            calls.append((data, camera, scene_option))
+            # A debug overlay must append to, not replace, the model scene.
+            self.scene.geoms[0].pos[:] = [5, 6, 7]
+            self.scene.ngeom = 1
 
         def render(self):
+            assert self.scene.ngeom == 2
+            np.testing.assert_array_equal(self.scene.geoms[0].pos, [5, 6, 7])
+            np.testing.assert_array_equal(self.scene.geoms[1].pos, [1, 2, 3])
+            assert self.scene.geoms[1].label == "goal"
+            np.testing.assert_array_equal(self.scene.flags, debug_scene.flags)
             return np.zeros((2, 2, 3), dtype=np.uint8)
 
         def close(self):
@@ -33,23 +44,41 @@ def close(self):
     monkeypatch.setattr(plt, "imsave", lambda path, pixels: None)
 
     camera = object()
+    option = object()
     data = object()
     simulator = object.__new__(MujocoSimulator)
-    simulator.model = object()
+    simulator.model = mujoco.MjModel.from_xml_string("<mujoco/>")
     simulator.data = data
-    simulator.viewer = SimpleNamespace(cam=camera, lock=lambda: nullcontext())
+    simulator._user_recording_frame = 0
+    debug_scene = mujoco.MjvScene(simulator.model, maxgeom=4)
+    mujoco.mjv_initGeom(
+        debug_scene.geoms[0],
+        mujoco.mjtGeom.mjGEOM_SPHERE,
+        np.ones(3),
+        np.array([1.0, 2.0, 3.0]),
+        np.eye(3).ravel(),
+        np.ones(4),
+    )
+    debug_scene.geoms[0].label = "goal"
+    debug_scene.ngeom = 1
+    debug_scene.flags[mujoco.mjtRndFlag.mjRND_SHADOW] = False
+    simulator.viewer = SimpleNamespace(
+        cam=camera, opt=option, user_scn=debug_scene, lock=lambda: nullcontext()
+    )
+    if viewport_size is not None:
+        simulator.viewer.viewport = mujoco.MjrRect(0, 0, *viewport_size)
     simulator._viewer_initialized = True
 
     simulator._write_viewport_to_file(str(tmp_path / "frame.png"))
 
-    assert calls == [(data, camera)]
+    assert calls == [(data, camera, option)]
 
 
 def test_viewport_capture_falls_back_without_viewer(monkeypatch, tmp_path):
     calls = []
 
     class FakeRenderer:
-        def __init__(self, model, *, height, width):
+        def __init__(self, model, *, height, width, max_geom):
             pass
 
         def update_scene(self, data, *, camera=-1):
@@ -66,11 +95,90 @@ def close(self):
 
     data = object()
     simulator = object.__new__(MujocoSimulator)
-    simulator.model = object()
+    simulator.model = mujoco.MjModel.from_xml_string("<mujoco/>")
     simulator.data = data
+    simulator._user_recording_frame = 0
     simulator.viewer = None
     simulator._viewer_initialized = False
 
     simulator._write_viewport_to_file(str(tmp_path / "frame.png"))
 
     assert calls == [(data, -1)]
+
+
+def test_viewport_capture_renders_markers_and_arrows(tmp_path):
+    """Exercise the real offscreen renderer, including hiding debug geometry."""
+    simulator = object.__new__(MujocoSimulator)
+    simulator.model = mujoco.MjModel.from_xml_string("<mujoco/>")
+    simulator.data = mujoco.MjData(simulator.model)
+    mujoco.mj_forward(simulator.model, simulator.data)
+    scene = mujoco.MjvScene(simulator.model, maxgeom=4)
+    simulator._marker_overflow_warned = False
+    simulator._add_mujoco_marker_geom(
+        scene,
+        "sphere",
+        "regular",
+        np.array([0.0, -0.2, 0.0]),
+        np.array([1.0, 0.0, 0.0, 0.0]),
+        np.array([1.0, 0.0, 0.0, 1.0]),
+    )
+    simulator._add_mujoco_marker_geom(
+        scene,
+        "arrow",
+        "small",
+        np.array([0.0, 0.2, 0.0]),
+        np.array([1.0, 0.0, 0.0, 0.0]),
+        np.array([0.0, 1.0, 0.0, 1.0]),
+    )
+    camera = mujoco.MjvCamera()
+    camera.distance = 2
+    camera.azimuth = 90
+    camera.elevation = -30
+    camera.lookat[:] = [0.3, 0, 0]
+    simulator.viewer = SimpleNamespace(
+        cam=camera, opt=mujoco.MjvOption(), user_scn=scene, lock=lambda: nullcontext()
+    )
+    simulator._viewer_initialized = True
+    simulator._user_recording_frame = 0
+    simulator.viewer.viewport = mujoco.MjrRect(0, 0, 1281, 721)
+    path = tmp_path / "visible.png"
+    simulator._write_viewport_to_file(str(path))
+    pixels = plt.imread(path)
+    assert pixels.shape[:2] == (720, 1280)
+    assert np.count_nonzero(pixels[..., 0] > pixels[..., 1] + 0.1) > 20
+    assert np.count_nonzero(pixels[..., 1] > pixels[..., 0] + 0.1) > 20
+    scene.ngeom = 0
+    simulator._user_recording_frame = 1
+    simulator.viewer.viewport = mujoco.MjrRect(0, 0, 640, 480)
+    simulator._write_viewport_to_file(str(tmp_path / "hidden.png"))
+    hidden = plt.imread(tmp_path / "hidden.png")
+    assert hidden.shape == pixels.shape
+    assert np.count_nonzero(hidden[..., :3]) == 0
+    simulator._user_recording_frame = 0
+    simulator._write_viewport_to_file(str(tmp_path / "next_clip.png"))
+    assert plt.imread(tmp_path / "next_clip.png").shape[:2] == (480, 640)
+
+
+def test_viewport_capture_closes_renderer_on_error(monkeypatch, tmp_path):
+    closed = []
+
+    class FakeRenderer:
+        def __init__(self, *args, **kwargs):
+            pass
+
+        def update_scene(self, data):
+            raise RuntimeError("render failure")
+
+        def close(self):
+            closed.append(True)
+
+    monkeypatch.setattr(mujoco, "Renderer", FakeRenderer)
+    simulator = object.__new__(MujocoSimulator)
+    simulator.model = mujoco.MjModel.from_xml_string("<mujoco/>")
+    simulator.data = object()
+    simulator.viewer = None
+    simulator._viewer_initialized = False
+    simulator._user_recording_frame = 0
+    with pytest.raises(RuntimeError, match="render failure"):
+        simulator._write_viewport_to_file(str(tmp_path / "frame.png"))
+    assert closed == [True]
diff --git a/protomotions/tests/test_mujoco_root_ang_vel.py b/protomotions/tests/test_mujoco_root_ang_vel.py
new file mode 100644
index 000000000..5cb16be20
--- /dev/null
+++ b/protomotions/tests/test_mujoco_root_ang_vel.py
@@ -0,0 +1,111 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""MuJoCo backend: root angular velocity is world-frame on reset, read and push.
+
+A free joint's qvel[3:6] is angular velocity in the BODY frame, while ProtoMotions'
+root_ang_vel is world-frame. Writing the world vector straight into qvel[3:6] spins a
+robot that was reset into a turned or tilted motion frame about the wrong axis. These
+tests build the real simulator from the shipped SMPL assets, which use primitive geoms
+and so load without Git LFS payloads, and check the pelvis spin with MuJoCo's own
+world-oriented velocity.
+"""
+
+import numpy as np
+import pytest
+import torch
+
+mujoco = pytest.importorskip("mujoco")
+
+from protomotions.components.scene_lib import SceneLib  # noqa: E402
+from protomotions.robot_configs.factory import robot_config  # noqa: E402
+from protomotions.simulator.base_simulator.simulator_state import (  # noqa: E402
+    ResetState,
+    StateConversion,
+)
+from protomotions.simulator.mujoco.config import MujocoSimulatorConfig  # noqa: E402
+from protomotions.simulator.mujoco.simulator import MujocoSimulator  # noqa: E402
+
+# Tilted and turned well away from upright (xyzw), so any frame mix-up shows.
+ROOT_ROT = torch.nn.functional.normalize(
+    torch.tensor([[0.3, -0.25, 0.8, 0.45]]), dim=-1
+)
+ROOT_ANG_VEL = torch.tensor([[0.9, -0.4, 1.3]])  # world frame, rad/s
+
+
+def _make_sim():
+    rc = robot_config("smpl")
+    config = MujocoSimulatorConfig(
+        num_envs=1,
+        headless=True,
+        experiment_name="test_mujoco_root_ang_vel",
+        sim=rc.simulation_params.mujoco,
+    )
+    sim = MujocoSimulator(
+        config=config,
+        robot_config=rc,
+        terrain=None,
+        device=torch.device("cpu"),
+        scene_lib=SceneLib.empty(num_envs=1, device="cpu", terrain=None),
+    )
+    sim._initialize_with_markers({})
+    return sim
+
+
+def _reset(sim, root_ang_vel):
+    n = sim._num_actuated_dofs
+    sim.reset_envs(
+        ResetState(
+            root_pos=torch.tensor([[0.0, 0.0, 1.0]]),
+            root_rot=ROOT_ROT.clone(),
+            root_vel=torch.zeros(1, 3),
+            root_ang_vel=root_ang_vel.clone(),
+            dof_pos=torch.zeros(1, n),
+            dof_vel=torch.zeros(1, n),
+            state_conversion=StateConversion.COMMON,
+        )
+    )
+
+
+def _pelvis_world_ang_vel(sim):
+    """Pelvis angular velocity in world orientation, as MuJoCo itself computes it."""
+    mujoco.mj_forward(sim.model, sim.data)
+    assert sim.model.jnt_type[0] == mujoco.mjtJoint.mjJNT_FREE
+    body = sim.model.jnt_bodyid[0]  # the root body, which the free joint moves
+    velocity = np.zeros(6)  # [angular, linear]
+    mujoco.mj_objectVelocity(
+        sim.model, sim.data, mujoco.mjtObj.mjOBJ_XBODY, body, velocity, 0
+    )
+    return velocity[:3]
+
+
+def test_reset_spins_the_pelvis_at_the_requested_world_angular_velocity():
+    sim = _make_sim()
+
+    _reset(sim, ROOT_ANG_VEL)
+
+    np.testing.assert_allclose(
+        _pelvis_world_ang_vel(sim), ROOT_ANG_VEL[0].numpy(), atol=1e-5
+    )
+
+
+def test_root_angular_velocity_reads_back_in_the_world_frame():
+    sim = _make_sim()
+
+    _reset(sim, ROOT_ANG_VEL)
+
+    read_back = sim.get_root_state().root_ang_vel[0]
+    np.testing.assert_allclose(read_back.numpy(), ROOT_ANG_VEL[0].numpy(), atol=1e-5)
+
+
+def test_angular_push_is_applied_in_the_world_frame():
+    sim = _make_sim()
+    _reset(sim, torch.zeros(1, 3))
+
+    sim._apply_root_velocity_impulse(
+        torch.zeros(1, 3), ROOT_ANG_VEL.clone(), torch.tensor([0])
+    )
+
+    np.testing.assert_allclose(
+        _pelvis_world_ang_vel(sim), ROOT_ANG_VEL[0].numpy(), atol=1e-5
+    )
diff --git a/protomotions/tests/test_newton_explicit_control.py b/protomotions/tests/test_newton_explicit_control.py
new file mode 100644
index 000000000..627f7b6fa
--- /dev/null
+++ b/protomotions/tests/test_newton_explicit_control.py
@@ -0,0 +1,108 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Newton must step in its explicit control modes, TORQUE and PROPORTIONAL.
+
+Actions reach the simulator already processed: PD targets for PROPORTIONAL,
+torques for TORQUE. The other backends go through the base ``_apply_control``,
+which uses them as-is. Newton has its own path, because it applies these modes
+from Warp kernels captured in a CUDA graph: it writes the actions into a
+persistent Warp buffer that the kernels read on every substep. That path kept
+calling the scaling helpers the ActionProcessor refactor deleted, so the first
+step in either mode raised AttributeError. These tests step a real G1 and check
+what landed in that buffer.
+
+Runs on CUDA when available, otherwise CPU. On CPU the first run spends about
+a minute compiling Warp kernels; later runs take a few seconds each.
+"""
+
+import pytest
+import torch
+
+from protomotions.robot_configs.base import ControlType
+
+NUM_ENVS = 2
+
+
+def _make_simulator(control_type: ControlType):
+    pytest.importorskip("warp")
+    pytest.importorskip("newton")
+    from protomotions.components.scene_lib import SceneLib
+    from protomotions.components.terrains.config import TerrainConfig
+    from protomotions.components.terrains.terrain import Terrain
+    from protomotions.robot_configs.factory import robot_config
+    from protomotions.simulator.newton.config import (
+        NewtonSimParams,
+        NewtonSimulatorConfig,
+    )
+    from protomotions.simulator.newton.simulator import NewtonSimulator
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    robot_cfg = robot_config("g1")
+    robot_cfg.control.control_type = control_type
+    terrain = Terrain(
+        config=TerrainConfig(
+            map_length=20.0,
+            map_width=20.0,
+            border_size=40.0,
+            num_levels=1,
+            num_terrains=1,
+            terrain_proportions=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
+            horizontal_scale=0.1,
+            vertical_scale=0.005,
+        ),
+        num_envs=NUM_ENVS,
+        device=device,
+    )
+    simulator = NewtonSimulator(
+        config=NewtonSimulatorConfig(
+            num_envs=NUM_ENVS,
+            headless=True,
+            sim=NewtonSimParams(fps=120, decimation=4),
+            experiment_name="newton_explicit_control_test",
+        ),
+        robot_config=robot_cfg,
+        terrain=terrain,
+        device=device,
+        scene_lib=SceneLib.empty(
+            num_envs=NUM_ENVS, device=str(device), terrain=terrain
+        ),
+    )
+    simulator._initialize_with_markers({})
+    return simulator
+
+
+def _control_buffer(simulator) -> torch.Tensor:
+    import warp as wp
+
+    return wp.to_torch(simulator._pd_targets_wp).view(simulator.num_envs, -1)
+
+
+@pytest.mark.needs_lfs
+def test_torque_mode_applies_actions_as_clipped_torques():
+    simulator = _make_simulator(ControlType.TORQUE)
+    limits = simulator._torque_limits_common
+    # Distinct per-joint values so a wrong DOF reordering shows up, and the
+    # outer joints ask for up to twice their limit so clipping is exercised.
+    ramp = torch.linspace(-2.0, 2.0, limits.numel(), device=simulator.device)
+    actions = (ramp * limits).expand(NUM_ENVS, -1).clone()
+
+    simulator.step(actions)
+
+    expected = torch.clip(actions, -limits, limits)[
+        :, simulator.data_conversion.dof_convert_to_sim
+    ]
+    torch.testing.assert_close(_control_buffer(simulator), expected)
+
+
+@pytest.mark.needs_lfs
+def test_proportional_mode_applies_actions_as_pd_targets():
+    simulator = _make_simulator(ControlType.PROPORTIONAL)
+    num_dofs = simulator.robot_config.number_of_actions
+    targets = torch.linspace(-0.3, 0.3, num_dofs, device=simulator.device)
+    actions = targets.expand(NUM_ENVS, -1).clone()
+
+    simulator.step(actions)
+
+    expected = actions[:, simulator.data_conversion.dof_convert_to_sim]
+    torch.testing.assert_close(_control_buffer(simulator), expected)
diff --git a/protomotions/tests/test_newton_park_envs.py b/protomotions/tests/test_newton_park_envs.py
deleted file mode 100644
index 3179d04c7..000000000
--- a/protomotions/tests/test_newton_park_envs.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-
-"""Newton must not park evaluation envs.
-
-Parking (teleport to z<<0) exists to relieve PhysX's broadphase pair budget.
-Newton replicates environments as separate worlds, so there is no cross-env
-broadphase to reduce. A parked robot still free-falls with PD control active:
-joint velocities diverge and the resulting non-finite values persist in solver
-warm-start memory, permanently poisoning the parked envs. NewtonSimulator
-therefore overrides park_envs as a documented no-op; these tests pin that
-contract.
-"""
-
-from unittest.mock import MagicMock
-
-import pytest
-import torch
-
-
-def _newton_simulator_cls():
-    pytest.importorskip("warp")
-    pytest.importorskip("newton")
-    from protomotions.simulator.newton.simulator import NewtonSimulator
-
-    return NewtonSimulator
-
-
-def test_park_envs_is_overridden():
-    from protomotions.simulator.base_simulator.simulator import Simulator
-
-    NewtonSimulator = _newton_simulator_cls()
-    assert NewtonSimulator.park_envs is not Simulator.park_envs
-
-
-def test_park_envs_touches_no_simulator_state():
-    NewtonSimulator = _newton_simulator_cls()
-    mock_sim = MagicMock()
-    env_ids = torch.arange(4)
-
-    result = NewtonSimulator.park_envs(mock_sim, env_ids)
-
-    assert result is None
-    assert mock_sim.mock_calls == []
diff --git a/protomotions/tests/test_noisy_current_state_view.py b/protomotions/tests/test_noisy_current_state_view.py
new file mode 100644
index 000000000..50ded0b31
--- /dev/null
+++ b/protomotions/tests/test_noisy_current_state_view.py
@@ -0,0 +1,128 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""The noisy CurrentStateView must carry the dedicated root/anchor noise.
+
+apply_observation_noise noises anchor_rot, root_local_ang_vel and
+anchor_local_ang_vel on their own (anchor_rot_noise, root_ang_vel_noise,
+anchor_ang_vel_noise). CurrentStateView used to re-derive those three from the
+whole-body tensors, so actor observations such as projected gravity and the
+pelvis gyro saw clean values even with that noise configured.
+"""
+
+import pytest
+import torch
+
+from protomotions.envs.context_views import CurrentStateView
+from protomotions.envs.obs import compute_humanoid_reduced_coords_observations
+from protomotions.envs.obs.observation_noise import apply_observation_noise
+from protomotions.simulator.base_simulator.config import RobotNoiseConfig
+from protomotions.simulator.base_simulator.simulator_state import (
+    RobotState,
+    StateConversion,
+)
+
+NUM_ENVS, NUM_BODIES, NUM_DOFS = 64, 3, 4
+
+
+def _robot_state() -> RobotState:
+    rot = torch.nn.functional.normalize(torch.randn(NUM_ENVS, NUM_BODIES, 4), dim=-1)
+    return RobotState(
+        rigid_body_pos=torch.randn(NUM_ENVS, NUM_BODIES, 3),
+        rigid_body_rot=rot,
+        rigid_body_vel=torch.randn(NUM_ENVS, NUM_BODIES, 3),
+        rigid_body_ang_vel=torch.randn(NUM_ENVS, NUM_BODIES, 3),
+        dof_pos=torch.randn(NUM_ENVS, NUM_DOFS),
+        dof_vel=torch.randn(NUM_ENVS, NUM_DOFS),
+        state_conversion=StateConversion.COMMON,
+    )
+
+
+def _reduced_coords_obs(view: CurrentStateView) -> torch.Tensor:
+    return compute_humanoid_reduced_coords_observations(
+        dof_pos=view.dof_pos,
+        dof_vel=view.dof_vel,
+        anchor_rot=view.anchor_rot,
+        root_local_ang_vel=view.root_local_ang_vel,
+        w_last=True,
+    )
+
+
+@pytest.mark.parametrize("anchor_idx", [0, 2])
+def test_noisy_view_uses_dedicated_root_and_anchor_noise(anchor_idx):
+    torch.manual_seed(0)
+    state = _robot_state()
+    # Only the dedicated channels are noised, so anything re-derived from the
+    # whole-body tensors would come out exactly clean.
+    noise_cfg = RobotNoiseConfig(
+        anchor_rot_noise=0.05, root_ang_vel_noise=0.2, anchor_ang_vel_noise=0.2
+    )
+    noisy = apply_observation_noise(noise_cfg, state, anchor_idx, torch.zeros(NUM_ENVS))
+
+    clean_view = CurrentStateView(state, anchor_idx)
+    noisy_view = CurrentStateView(noisy, anchor_idx)
+
+    for name in ("anchor_rot", "root_local_ang_vel", "anchor_local_ang_vel"):
+        assert getattr(noisy_view, name) is getattr(noisy, name), name
+        assert not torch.allclose(
+            getattr(noisy_view, name), getattr(clean_view, name)
+        ), name
+
+    # Projected gravity and the pelvis gyro are what the actor actually reads.
+    assert not torch.allclose(
+        _reduced_coords_obs(noisy_view), _reduced_coords_obs(clean_view)
+    )
+
+
+def test_anchor_only_noise_leaves_pelvis_gyro_bit_identical():
+    """The G1 BeyondMimic-style configs noise only dofs and the anchor.
+
+    For them the fix must change anchor_rot and nothing else the actor reads:
+    root_ang_vel_noise is 0 there, so the pelvis gyro must stay exactly clean.
+    """
+    torch.manual_seed(0)
+    state = _robot_state()
+    noise_cfg = RobotNoiseConfig(
+        dof_pos_noise=0.01,
+        dof_vel_noise=0.5,
+        anchor_ang_vel_noise=0.2,
+        anchor_rot_noise=0.05,
+    )
+    noisy = apply_observation_noise(noise_cfg, state, 2, torch.zeros(NUM_ENVS))
+
+    clean_view = CurrentStateView(state, anchor_idx=2)
+    noisy_view = CurrentStateView(noisy, anchor_idx=2)
+
+    assert torch.equal(noisy_view.root_local_ang_vel, clean_view.root_local_ang_vel)
+    assert torch.equal(noisy_view.root_rot, clean_view.root_rot)
+    assert not torch.allclose(noisy_view.anchor_rot, clean_view.anchor_rot)
+
+
+def test_noisy_view_follows_env_subset_selection():
+    torch.manual_seed(0)
+    state = _robot_state()
+    noise_cfg = RobotNoiseConfig(anchor_rot_noise=0.05, root_ang_vel_noise=0.2)
+    noisy = apply_observation_noise(noise_cfg, state, 1, torch.zeros(NUM_ENVS))
+    env_ids = torch.tensor([5, 2, 40])
+
+    subset_view = CurrentStateView(noisy.select(env_ids), anchor_idx=1)
+
+    assert torch.equal(subset_view.anchor_rot, noisy.anchor_rot[env_ids])
+    assert torch.equal(
+        subset_view.root_local_ang_vel, noisy.root_local_ang_vel[env_ids]
+    )
+
+
+def test_clean_view_still_derives_root_and_anchor_from_state():
+    torch.manual_seed(0)
+    state = _robot_state()
+
+    view = CurrentStateView(state, anchor_idx=2)
+
+    assert torch.equal(view.anchor_rot, state.rigid_body_rot[:, 2, :])
+    # With no noise configured the NoisyObservations fields are the clean
+    # values, so the noisy view must match the clean one exactly.
+    no_noise = apply_observation_noise(None, state, 2, torch.zeros(NUM_ENVS))
+    noisy_view = CurrentStateView(no_noise, anchor_idx=2)
+    for name in ("anchor_rot", "root_local_ang_vel", "anchor_local_ang_vel"):
+        assert torch.equal(getattr(noisy_view, name), getattr(view, name)), name
diff --git a/protomotions/tests/test_pretrained_model_cards.py b/protomotions/tests/test_pretrained_model_cards.py
index 550a2b608..7d8849108 100644
--- a/protomotions/tests/test_pretrained_model_cards.py
+++ b/protomotions/tests/test_pretrained_model_cards.py
@@ -19,6 +19,7 @@
 README_PATH = REPO_ROOT / "README.md"
 
 REQUIRED_SECTIONS = (
+    "License",
     "Overview",
     "Intended Use",
     "Training",
diff --git a/protomotions/tests/test_prior_peft_agent_helpers.py b/protomotions/tests/test_prior_peft_agent_helpers.py
index af54e9cdf..2578f7f2f 100644
--- a/protomotions/tests/test_prior_peft_agent_helpers.py
+++ b/protomotions/tests/test_prior_peft_agent_helpers.py
@@ -1227,6 +1227,9 @@ def experience_buffer_keys(self):
     agent.best_evaluated_score = None
     agent._should_stop = False
     agent.fabric = _Fabric()
+    # A dir with no last.ckpt so the loop-entry "reached training" save fires (it is
+    # written only when absent).
+    agent.root_dir = Path("nonexistent_fit_test_dir")
     agent.env = _FitEnv()
     agent.evaluator = _Evaluator()
     agent.model = _FitModel()
@@ -1360,6 +1363,9 @@ def experience_buffer_keys(self):
     agent.best_evaluated_score = None
     agent._should_stop = True
     agent.fabric = _Fabric()
+    # A dir with no last.ckpt so the loop-entry "reached training" save fires (it is
+    # written only when absent).
+    agent.root_dir = Path("nonexistent_fit_test_dir")
     agent.env = _FitEnv()
     agent.evaluator = None
     agent.model = _FitModel()
diff --git a/protomotions/tests/test_retargeting_keypoints.py b/protomotions/tests/test_retargeting_keypoints.py
new file mode 100644
index 000000000..70b13317f
--- /dev/null
+++ b/protomotions/tests/test_retargeting_keypoints.py
@@ -0,0 +1,87 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+
+from protomotions.utils.retargeting_keypoints import (
+    SOMA_PELVIS_KEYPOINT_SHIFT,
+    soma_pelvis_keypoint_on_hips,
+)
+
+
+# Every keypoint file on disk was written with this shift.
+SHIFT_ON_DISK = 0.07
+
+
+def _random_rotations(rng, shape):
+    q = rng.normal(size=(*shape, 4))
+    q /= np.linalg.norm(q, axis=-1, keepdims=True)
+    w, x, y, z = np.moveaxis(q, -1, 0)
+    return np.stack(
+        [
+            np.stack(
+                [1 - 2 * (y * y + z * z), 2 * (x * y - w * z), 2 * (x * z + w * y)], -1
+            ),
+            np.stack(
+                [2 * (x * y + w * z), 1 - 2 * (x * x + z * z), 2 * (y * z - w * x)], -1
+            ),
+            np.stack(
+                [2 * (x * z - w * y), 2 * (y * z + w * x), 1 - 2 * (x * x + y * y)], -1
+            ),
+        ],
+        -2,
+    )
+
+
+def _soma_keypoints(pelvis_shift_y, frames=40, seed=0):
+    """Pelvis keypoint, two hip joints and one more keypoint for randomly posed
+    Hips frames; the hips sit 2.6 cm in front of the Hips joint (local -y). The
+    other keypoints get their own orientations, as in real files."""
+    rng = np.random.default_rng(seed)
+    R = _random_rotations(rng, (frames,))
+    hips = rng.normal(size=(frames, 3))
+    local = np.array(
+        [
+            [0, pelvis_shift_y, 0],
+            [0.09, -0.026, -0.05],
+            [-0.09, -0.026, -0.05],
+            [0, -0.2, 0.3],
+        ]
+    )
+    positions = hips[:, None] + np.einsum("tij,kj->tki", R, local)
+    others = _random_rotations(rng, (frames, len(local) - 1))
+    orientations = np.concatenate([R[:, None], others], axis=1)
+    return positions.astype(np.float32), orientations.astype(np.float32), hips
+
+
+def test_shift_matches_the_files_on_disk():
+    assert SOMA_PELVIS_KEYPOINT_SHIFT == (0.0, SHIFT_ON_DISK, 0.0)
+
+
+def test_shifted_pelvis_keypoint_moves_onto_the_hips_joint():
+    positions, orientations, hips = _soma_keypoints(SHIFT_ON_DISK)
+    before = positions.copy()
+    out = soma_pelvis_keypoint_on_hips(positions, orientations)
+    np.testing.assert_allclose(out[:, 0], hips, atol=1e-5)
+    np.testing.assert_array_equal(out[:, 1:], positions[:, 1:])
+    np.testing.assert_array_equal(positions, before)  # input left untouched
+
+
+def test_unshifted_keypoints_are_unchanged():
+    positions, orientations, _ = _soma_keypoints(0.0)
+    out = soma_pelvis_keypoint_on_hips(positions, orientations)
+    np.testing.assert_array_equal(out, positions)
+    # Applying it twice is harmless.
+    shifted, orientations, _ = _soma_keypoints(SHIFT_ON_DISK)
+    once = soma_pelvis_keypoint_on_hips(shifted, orientations)
+    np.testing.assert_array_equal(
+        soma_pelvis_keypoint_on_hips(once, orientations), once
+    )
+
+
+def test_other_skeletons_are_rejected():
+    # Bones v24 keypoint files put the hips 7 cm in front of the pelvis keypoint.
+    positions, orientations, _ = _soma_keypoints(0.044)
+    with pytest.raises(ValueError, match="Not SOMA keypoints"):
+        soma_pelvis_keypoint_on_hips(positions, orientations)
diff --git a/protomotions/tests/test_reward_kernels.py b/protomotions/tests/test_reward_kernels.py
index 57bd6cb0a..df7eb4599 100644
--- a/protomotions/tests/test_reward_kernels.py
+++ b/protomotions/tests/test_reward_kernels.py
@@ -206,6 +206,14 @@ def test_regularization_rewards_and_helpers():
         regularization.compute_action_smoothness(current_action, previous_action),
         torch.tensor([2.0, 5.0]),
     )
+    torch.testing.assert_close(
+        regularization.compute_action_rate_l2(current_action, previous_action),
+        torch.tensor([4.0, 25.0]),
+    )
+    torch.testing.assert_close(
+        regularization.compute_action_rate_l2(current_action, current_action),
+        torch.zeros(2),
+    )
     assert torch.allclose(
         regularization.compute_action_smoothness_logmeanexp(
             current_action,
diff --git a/protomotions/tests/test_robot_marker_inertias.py b/protomotions/tests/test_robot_marker_inertias.py
new file mode 100644
index 000000000..596dea0c0
--- /dev/null
+++ b/protomotions/tests/test_robot_marker_inertias.py
@@ -0,0 +1,127 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Shared MJCF inertias must not depend on a simulator's default link mass."""
+
+from pathlib import Path
+import xml.etree.ElementTree as ET
+
+from dm_control import mjcf
+import mujoco
+import numpy as np
+import pytest
+
+from protomotions.simulator.base_simulator.robot_mass_properties import (
+    prepare_robot_mjcf,
+    _read_mjcf,
+)
+
+
+ASSET_DIR = Path(__file__).resolve().parents[1] / "data" / "assets" / "mjcf"
+MARKER_ASSETS = {
+    "g1_bm.xml": ("head", "left_rubber_hand", "right_rubber_hand"),
+    "g1_bm_box_feet.xml": ("head", "left_rubber_hand", "right_rubber_hand"),
+    "g1_bm_no_mesh_box_feet.xml": ("head", "left_rubber_hand", "right_rubber_hand"),
+    "g1_holo_compat.xml": ("head", "left_rubber_hand", "right_rubber_hand"),
+    "g1_holo_compat_box_feet.xml": ("head", "left_rubber_hand", "right_rubber_hand"),
+    "h1_2.xml": ("head_aux",),
+    "h1_2_box_feet.xml": ("head_aux",),
+    "h1_2_no_mesh_box_feet.xml": ("head_aux",),
+}
+
+
+def asset_case(path):
+    """Keep the XML checks in public CI and mesh compilation in LFS CI."""
+    root = ET.parse(path).getroot()
+    marks = pytest.mark.needs_lfs if any(e.get("file") for e in root.iter()) else ()
+    return pytest.param(path, marks=marks, id=path.name)
+
+
+def compile_body_inertias(path):
+    """Compile body properties without unrelated sensor/contact references.
+
+    Some robot-only assets refer to an IMU or ground injected by the runtime.
+    Remove only non-inertial sections in this test; keep bodies, joints, geoms,
+    compiler settings and defaults so mass inference is exercised normally.
+    dm_control resolves includes and packages mesh dependencies for MuJoCo.
+    """
+    root = _read_mjcf(path)
+    for tag in ("sensor", "contact", "actuator", "tendon", "equality", "keyframe"):
+        for element in root.findall(tag):
+            root.remove(element)
+    source = mjcf.from_xml_string(ET.tostring(root, encoding="unicode"))
+    return mujoco.MjModel.from_xml_string(source.to_xml_string(), source.get_assets())
+
+
+@pytest.mark.parametrize(
+    "path", [asset_case(ASSET_DIR / name) for name in sorted(MARKER_ASSETS)]
+)
+def test_markers_have_explicit_one_gram_inertias(path, tmp_path):
+    original = path.read_bytes()
+    prepared = prepare_robot_mjcf(path, tmp_path)
+    root = ET.parse(prepared).getroot()
+    assert path.read_bytes() == original
+    for name in MARKER_ASSETS[path.name]:
+        body = root.find(f".//body[@name='{name}']")
+        assert body is not None
+        assert body.find("joint") is None and body.find("freejoint") is None
+        inertial = body.find("inertial")
+        assert inertial is not None
+        assert float(inertial.get("mass")) == 0.001
+        np.testing.assert_array_equal(
+            np.fromstring(inertial.get("diaginertia"), sep=" "), [4e-8] * 3
+        )
+        np.testing.assert_array_equal(
+            np.fromstring(inertial.get("pos"), sep=" "), [0.0] * 3
+        )
+
+
+@pytest.mark.parametrize(
+    "path", [asset_case(p) for p in sorted(ASSET_DIR.glob("*.xml"))]
+)
+def test_robot_links_have_positive_mass_and_inertia(path, tmp_path):
+    original = compile_body_inertias(path)
+    model = compile_body_inertias(prepare_robot_mjcf(path, tmp_path))
+    # The world body is intentionally static and has no inertial properties.
+    assert np.all(model.body_mass[1:] > 0), path.name
+    assert np.all(model.body_inertia[1:] > 0), path.name
+    for name in MARKER_ASSETS.get(path.name, ()):
+        np.testing.assert_array_equal(model.body(name).mass, [0.001])
+        np.testing.assert_allclose(model.body(name).inertia, [4e-8] * 3, rtol=1e-12)
+        np.testing.assert_array_equal(model.body(name).ipos, [0.0] * 3)
+    physical = original.body_mass > 0
+    np.testing.assert_allclose(model.body_mass[physical], original.body_mass[physical])
+    np.testing.assert_allclose(
+        model.body_inertia[physical], original.body_inertia[physical]
+    )
+
+
+@pytest.mark.parametrize("inference", ["auto", "true"])
+def test_normalization_resolves_defaults_includes_and_geometry(tmp_path, inference):
+    source = tmp_path / "source"
+    source.mkdir()
+    (source / "child.xml").write_text(
+        '<mujocoinclude><body name="marker" childclass="massless">'
+        '<geom type="sphere" size="0.0001"/></body></mujocoinclude>'
+    )
+    path = source / "robot.xml"
+    path.write_text(
+        f'<mujoco><compiler inertiafromgeom="{inference}"/>'
+        '<default><default class="massless"><geom density="0"/></default></default>'
+        '<worldbody><body name="physical"><freejoint/>'
+        '<geom type="sphere" size="0.1" density="1000"/>'
+        '<include file="child.xml"/></body></worldbody></mujoco>'
+    )
+    original = compile_body_inertias(path)
+    prepared = prepare_robot_mjcf(path, tmp_path / "prepared")
+    model = compile_body_inertias(prepared)
+    np.testing.assert_allclose(
+        model.body("physical").mass, original.body("physical").mass
+    )
+    np.testing.assert_allclose(
+        model.body("physical").inertia, original.body("physical").inertia
+    )
+    np.testing.assert_array_equal(model.body("marker").mass, [0.001])
+    np.testing.assert_array_equal(model.body("marker").inertia, [4e-8] * 3)
+    # Preparing an already normalized asset is a no-op.
+    assert prepare_robot_mjcf(prepared, tmp_path / "second") == prepared
diff --git a/protomotions/tests/test_simulator_factory_and_utils.py b/protomotions/tests/test_simulator_factory_and_utils.py
index a88bd70d4..4392c73ba 100644
--- a/protomotions/tests/test_simulator_factory_and_utils.py
+++ b/protomotions/tests/test_simulator_factory_and_utils.py
@@ -126,6 +126,21 @@ def test_update_simulator_config_for_test_adds_missing_params_and_new_fields(mon
         simulator_factory.update_simulator_config_for_test(current, "mujoco", robot_config)
 
 
+@pytest.mark.parametrize(
+    "train_sim, sim, warns",
+    [
+        (_FakeSimParams(120, 4), _FakeSimParams(1000, 20), True),  # 30 -> 50 Hz
+        (_FakeSimParams(120, 4), _FakeSimParams(960, 32), False),  # 30 -> 30 Hz
+    ],
+)
+def test_warn_on_control_rate_change(caplog, train_sim, sim, warns):
+    with caplog.at_level("WARNING", logger=simulator_factory.__name__):
+        warned = simulator_factory.warn_on_control_rate_change(train_sim, sim, "mujoco")
+
+    assert warned is warns
+    assert ("Control rate changes from 30 Hz" in caplog.text) is warns
+
+
 def test_build_motion_data_maps_recorded_fields_and_fills_missing_values():
     recorded_motion = {
         "gts": [torch.ones(2, 3), torch.ones(2, 3) * 2.0],
diff --git a/protomotions/tests/test_train_agent_helpers.py b/protomotions/tests/test_train_agent_helpers.py
index ef2feae5e..92dbd1bf6 100644
--- a/protomotions/tests/test_train_agent_helpers.py
+++ b/protomotions/tests/test_train_agent_helpers.py
@@ -414,6 +414,34 @@ def test_detect_checkpoint_mode_handles_fresh_warm_start_and_resume(
         detect_checkpoint_mode(SimpleNamespace(checkpoint=None), missing_config_dir)
 
 
+def test_resume_keeps_kit_thread_count_given_on_the_command_line(
+    monkeypatch,
+    tmp_path,
+):
+    module = _load_train_agent_globals(monkeypatch, tmp_path)
+    detect_checkpoint_mode = module["detect_checkpoint_mode"]
+    resume_dir = tmp_path / "resume"
+    resume_dir.mkdir()
+    (resume_dir / "last.ckpt").write_text("checkpoint")
+
+    def resume(saved, cli):
+        (resume_dir / "config.yaml").write_text(
+            json.dumps({"batch_size": 64, "kit_thread_count": saved})
+        )
+        args = SimpleNamespace(checkpoint=None, batch_size=8, kit_thread_count=cli)
+        assert detect_checkpoint_mode(args, resume_dir)[0] == "resume"
+        assert args.batch_size == 64
+        return args.kit_thread_count
+
+    # The fix for a run that starts aborting at startup is to add the flag to the
+    # resume command; the saved None from the original launch must not erase it.
+    assert resume(saved=None, cli=1) == 1
+    assert resume(saved=4, cli=1) == 1
+    # Without the flag, the saved value is used as before.
+    assert resume(saved=1, cli=None) == 1
+    assert resume(saved=None, cli=None) is None
+
+
 def test_load_experiment_module_loads_file_and_rejects_missing_paths(
     monkeypatch,
     tmp_path,
diff --git a/protomotions/tests/test_train_slurm_site.py b/protomotions/tests/test_train_slurm_site.py
index 24bb741d6..86a3367b6 100644
--- a/protomotions/tests/test_train_slurm_site.py
+++ b/protomotions/tests/test_train_slurm_site.py
@@ -271,35 +271,33 @@ def test_cli_account_overrides_missing_site_account(tmp_path, monkeypatch):
 
 
 def test_startup_site_keys_are_accepted_and_flag_beats_site(tmp_path, monkeypatch, capsys):
-    # A site file may set the four startup knobs; the loader (which rejects unknown
+    # A site file may set the three startup knobs; the loader (which rejects unknown
     # keys) must accept them because they are in SITE_DEFAULTS.
     site_on = FLAT_SITE + (
         "startup_max_attempts: 8\n"
-        "startup_stall_min: 12\n"
         "startup_repeat_limit: 4\n"
         "startup_retry_backoff_sec: 5\n"
     )
     loaded = ts.load_site_config(_write(tmp_path, site_on))
     assert loaded["startup_max_attempts"] == 8
-    assert loaded["startup_stall_min"] == 12
+    assert loaded["startup_repeat_limit"] == 4
 
     # With no flag, the site value turns the feature on: the emitted script wraps
-    # srun and the worst-case line is printed.
+    # srun in the shared run_with_retry and the worst-case line is printed.
     _run_main(monkeypatch, tmp_path, _write(tmp_path, site_on), ["--simulator", "isaaclab"])
     out = capsys.readouterr().out
-    assert "run_with_startup_retry" in out
     assert "up to 8 attempts" in out
 
-    # An explicit flag beats the site value: turning both knobs off leaves the
-    # default, byte-identical (unwrapped) script.
+    # An explicit flag beats the site value: turning it off leaves the default,
+    # byte-identical (unwrapped) script -- no worst-case line.
     _run_main(
         monkeypatch,
         tmp_path,
         _write(tmp_path, site_on),
-        ["--simulator", "isaaclab", "--startup-max-attempts", "1", "--startup-stall-min", "0"],
+        ["--simulator", "isaaclab", "--startup-max-attempts", "1"],
     )
     out = capsys.readouterr().out
-    assert "run_with_startup_retry" not in out
+    assert "up to" not in out
 
 
 # ---------------------------------------------------------------------------
diff --git a/protomotions/tests/test_train_slurm_startup_retry.py b/protomotions/tests/test_train_slurm_startup_retry.py
index 0bf72f06c..473605b24 100644
--- a/protomotions/tests/test_train_slurm_startup_retry.py
+++ b/protomotions/tests/test_train_slurm_startup_retry.py
@@ -1,26 +1,20 @@
 # SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-"""Behavioural tests for the opt-in startup retry loop + startup watchdog.
+"""Wiring tests for the SLURM launcher's opt-in startup retry.
 
-train_slurm.py wraps srun in run_with_startup_retry (produced by
-_startup_retry_bash) when --startup-max-attempts > 1 or --startup-stall-min > 0.
-The bash is exercised here with a fake command -- a small script that appends
-chosen lines to $LOGFILE and exits with a chosen code -- and the watchdog's poll
-interval / stall window are overridden to sub-second values, exactly the pattern
-test_train_slurm_watchdog.py uses. The last case is the acceptance gate: with the
-feature off (the default) generate_slurm_script emits a byte-identical script."""
+The retry LOGIC itself lives in scripts/cluster/retry_lib.sh and is exercised by
+test_cluster_retry_lib.py. These tests only check that train_slurm.py wires it up
+correctly: with the feature off the emitted script is byte-identical to the
+no-retry path, and with it on the batch layer sources the shared lib, wraps srun in
+run_with_retry, and exports the RETRY_* knobs (break-on-progress, the checkpoint
+path, the exception fast-fail limit). They load train_slurm.py directly the same
+way test_train_slurm_site.py does -- no SSH, no cluster."""
 
 import importlib.util
-import os
-import shutil
-import subprocess
-import sys
 from pathlib import Path
 from types import SimpleNamespace
 
-import pytest
-
 
 TRAIN_SLURM_PATH = Path(__file__).resolve().parents[1] / "train_slurm.py"
 
@@ -36,256 +30,6 @@ def _load_module():
 
 ts = _load_module()
 
-# The bash under test: kill_tree (a dependency) plus the retry loop.
-RETRY_BASH = ts._kill_tree_bash() + "\n" + ts._startup_retry_bash()
-
-pytestmark = pytest.mark.skipif(
-    sys.platform != "linux"
-    or shutil.which("bash") is None
-    or shutil.which("pgrep") is None,
-    reason="needs bash + pgrep + GNU date/stat/wc (the Linux CI runner has them)",
-)
-
-
-def _bash(body, env=None, timeout=40):
-    full = dict(os.environ)
-    if env:
-        full.update(env)
-    return subprocess.run(
-        ["bash", "-c", RETRY_BASH + "\n" + body],
-        env=full,
-        capture_output=True,
-        text=True,
-        timeout=timeout,
-    )
-
-
-def _exit_code(stdout):
-    for line in stdout.splitlines():
-        if line.startswith("EXIT="):
-            return int(line.split("=", 1)[1])
-    raise AssertionError(f"no EXIT= line in:\n{stdout}")
-
-
-def _fake_cmd(tmp_path, name, body):
-    """A fake srun: bump the attempt counter, record a RUN line, then `body`."""
-    p = tmp_path / name
-    p.write_text(
-        "#!/bin/bash\n"
-        'n=0; [ -f "$COUNTER" ] && n=$(cat "$COUNTER"); n=$((n + 1)); echo "$n" > "$COUNTER"\n'
-        'echo RUN >> "$RUNS"\n' + body + "\n"
-    )
-    p.chmod(0o755)
-    return p
-
-
-def _runs(tmp_path):
-    r = tmp_path / "runs"
-    return len(r.read_text().splitlines()) if r.exists() else 0
-
-
-def _env(tmp_path, log, **over):
-    e = {
-        "LOGFILE": str(log),
-        "COUNTER": str(tmp_path / "counter"),
-        "RUNS": str(tmp_path / "runs"),
-        "STARTUP_MAX_ATTEMPTS": "3",
-        "STARTUP_STALL_SEC": "0",
-        "STARTUP_BACKOFF_SEC": "0",
-        "STARTUP_REPEAT_LIMIT": "3",
-    }
-    e.update({k: str(v) for k, v in over.items()})
-    return e
-
-
-def _run(tmp_path, cmd, env):
-    return _bash(f'run_with_startup_retry "{cmd}"\necho "EXIT=$?"', env=env)
-
-
-# --- retry accounting --------------------------------------------------------
-
-
-def test_success_on_first_attempt_runs_once(tmp_path):
-    log = tmp_path / "log"
-    log.write_text("")
-    cmd = _fake_cmd(tmp_path, "ok.sh", "exit 0")
-    r = _run(tmp_path, cmd, _env(tmp_path, log))
-    assert _exit_code(r.stdout) == 0
-    assert _runs(tmp_path) == 1
-
-
-def test_passes_multiple_arguments_to_the_command(tmp_path):
-    log = tmp_path / "log"
-    log.write_text("")
-    # Production calls `run_with_startup_retry stdbuf -oL -eL srun ...` -- many argv
-    # elements. Run the whole argv, not just $1: bash -c '...' is 3 tokens.
-    env = _env(tmp_path, log)
-    r = _bash(
-        "run_with_startup_retry bash -c 'echo RUN >> \"$RUNS\"; exit 0'\necho \"EXIT=$?\"",
-        env=env,
-    )
-    assert _exit_code(r.stdout) == 0, r.stdout + r.stderr
-    assert _runs(tmp_path) == 1
-
-
-def test_two_failures_then_success(tmp_path):
-    log = tmp_path / "log"
-    log.write_text("")
-    cmd = _fake_cmd(tmp_path, "flaky.sh", 'case "$n" in 1|2) exit 1;; *) exit 0;; esac')
-    r = _run(tmp_path, cmd, _env(tmp_path, log, STARTUP_MAX_ATTEMPTS=5))
-    assert _exit_code(r.stdout) == 0
-    assert _runs(tmp_path) == 3
-
-
-def test_exhausting_attempts_returns_last_exit_code(tmp_path):
-    log = tmp_path / "log"
-    log.write_text("")
-    cmd = _fake_cmd(tmp_path, "fail.sh", "exit 7")
-    r = _run(tmp_path, cmd, _env(tmp_path, log, STARTUP_MAX_ATTEMPTS=3))
-    assert _exit_code(r.stdout) == 7
-    assert _runs(tmp_path) == 3
-
-
-def test_reached_training_then_failed_is_not_retried(tmp_path):
-    log = tmp_path / "log"
-    log.write_text("")
-    # Emits the training marker, then dies -- like a walltime kill mid-run. The
-    # array element resumes from checkpoint; retrying in place is wrong.
-    cmd = _fake_cmd(tmp_path, "midrun.sh", 'echo "Epoch 0: loss 1.2" >> "$LOGFILE"\nexit 1')
-    r = _run(tmp_path, cmd, _env(tmp_path, log, STARTUP_MAX_ATTEMPTS=5))
-    assert _exit_code(r.stdout) == 1
-    assert _runs(tmp_path) == 1
-
-
-# --- startup watchdog --------------------------------------------------------
-
-
-def test_startup_watchdog_kills_silent_child_and_retries(tmp_path):
-    log = tmp_path / "log"
-    log.write_text("")
-    cmd = _fake_cmd(tmp_path, "hang.sh", 'case "$n" in 1) sleep 100;; *) exit 0;; esac')
-    env = _env(tmp_path, log, STARTUP_MAX_ATTEMPTS=3, STARTUP_STALL_SEC=2, STARTUP_POLL_SEC="0.2")
-    r = _bash(f'run_with_startup_retry "{cmd}"\necho "EXIT=$?"', env=env, timeout=30)
-    assert _exit_code(r.stdout) == 0, r.stdout + r.stderr
-    assert _runs(tmp_path) == 2  # attempt 1 killed, attempt 2 succeeded
-
-
-def test_startup_watchdog_leaves_an_active_child_alone(tmp_path):
-    log = tmp_path / "log"
-    log.write_text("")
-    cmd = _fake_cmd(
-        tmp_path, "busy.sh", 'for i in $(seq 1 20); do echo tick >> "$LOGFILE"; sleep 0.1; done\nexit 0'
-    )
-    env = _env(tmp_path, log, STARTUP_MAX_ATTEMPTS=3, STARTUP_STALL_SEC=3, STARTUP_POLL_SEC="0.2")
-    r = _bash(f'run_with_startup_retry "{cmd}"\necho "EXIT=$?"', env=env, timeout=30)
-    assert _exit_code(r.stdout) == 0, r.stdout + r.stderr
-    assert _runs(tmp_path) == 1  # never killed
-
-
-def test_startup_watchdog_disarms_after_training_marker(tmp_path):
-    log = tmp_path / "log"
-    log.write_text("")
-    # Reaches training, then goes silent LONGER than the stall window. Because the
-    # watchdog disarmed at the marker, this must survive (the inner watchdog owns
-    # liveness now) rather than be killed and retried.
-    cmd = _fake_cmd(tmp_path, "disarm.sh", 'echo "Epoch 0: start" >> "$LOGFILE"\nsleep 2.5\nexit 0')
-    env = _env(tmp_path, log, STARTUP_MAX_ATTEMPTS=3, STARTUP_STALL_SEC=2, STARTUP_POLL_SEC="0.2")
-    r = _bash(f'run_with_startup_retry "{cmd}"\necho "EXIT=$?"', env=env, timeout=30)
-    assert _exit_code(r.stdout) == 0, r.stdout + r.stderr
-    assert _runs(tmp_path) == 1
-
-
-# --- the repeat check: retry flaky, refuse deterministic ---------------------
-
-
-def test_repeated_identical_exception_stops_at_repeat_limit(tmp_path):
-    log = tmp_path / "log"
-    log.write_text("")
-    cmd = _fake_cmd(tmp_path, "det.sh", 'echo "AssertionError: boom" >> "$LOGFILE"\nexit 1')
-    r = _run(
-        tmp_path, cmd, _env(tmp_path, log, STARTUP_MAX_ATTEMPTS=10, STARTUP_REPEAT_LIMIT=3)
-    )
-    assert _exit_code(r.stdout) == 1
-    assert _runs(tmp_path) == 3  # stopped at the repeat limit, not 10
-    assert "deterministic, not flaky" in r.stderr
-    assert "AssertionError: boom" in r.stderr  # the traceback is surfaced
-
-
-def test_native_abort_without_traceback_uses_full_budget(tmp_path):
-    log = tmp_path / "log"
-    log.write_text("")
-    # exit 250, an abort message but NO Python exception line -> the repeat check
-    # must not trip; this is the heap bug and it must keep retrying.
-    cmd = _fake_cmd(tmp_path, "abort.sh", 'echo "double free or corruption (out)" >> "$LOGFILE"\nexit 250')
-    r = _run(
-        tmp_path, cmd, _env(tmp_path, log, STARTUP_MAX_ATTEMPTS=4, STARTUP_REPEAT_LIMIT=3)
-    )
-    assert _exit_code(r.stdout) == 250
-    assert _runs(tmp_path) == 4
-
-
-def test_alternating_exceptions_do_not_trip_repeat_check(tmp_path):
-    log = tmp_path / "log"
-    log.write_text("")
-    cmd = _fake_cmd(
-        tmp_path,
-        "alt.sh",
-        'if [ $((n % 2)) -eq 1 ]; then echo "ValueError: odd" >> "$LOGFILE"; '
-        'else echo "KeyError: even" >> "$LOGFILE"; fi\nexit 1',
-    )
-    r = _run(
-        tmp_path, cmd, _env(tmp_path, log, STARTUP_MAX_ATTEMPTS=5, STARTUP_REPEAT_LIMIT=3)
-    )
-    assert _exit_code(r.stdout) == 1
-    assert _runs(tmp_path) == 5  # never two identical in a row -> full budget
-
-
-def test_message_less_exception_stops_at_repeat_limit(tmp_path):
-    log = tmp_path / "log"
-    log.write_text("")
-    # A bare `assert` prints just "AssertionError" -- no colon, no message. This is
-    # a common deterministic startup failure and must still be refused.
-    cmd = _fake_cmd(tmp_path, "bare.sh", 'echo "AssertionError" >> "$LOGFILE"\nexit 1')
-    r = _run(
-        tmp_path, cmd, _env(tmp_path, log, STARTUP_MAX_ATTEMPTS=10, STARTUP_REPEAT_LIMIT=3)
-    )
-    assert _exit_code(r.stdout) == 1
-    assert _runs(tmp_path) == 3
-    assert "deterministic, not flaky" in r.stderr
-
-
-def test_same_class_with_varying_message_stops_at_repeat_limit(tmp_path):
-    log = tmp_path / "log"
-    log.write_text("")
-    # Same exception class, a different message each attempt (a device id) -- the
-    # bug is the same, so the class-only comparison must still count it.
-    cmd = _fake_cmd(
-        tmp_path, "vary.sh", 'echo "RuntimeError: CUDA error on device $n" >> "$LOGFILE"\nexit 1'
-    )
-    r = _run(
-        tmp_path, cmd, _env(tmp_path, log, STARTUP_MAX_ATTEMPTS=10, STARTUP_REPEAT_LIMIT=3)
-    )
-    assert _exit_code(r.stdout) == 1
-    assert _runs(tmp_path) == 3
-
-
-def test_repeat_check_reads_only_the_current_attempt_slice(tmp_path):
-    log = tmp_path / "log"
-    log.write_text("")
-    # Odd attempts emit the exception, even attempts emit nothing (a native abort).
-    # Only if each attempt's exception is read from ITS OWN log slice does this stay
-    # under the repeat limit and use the full budget -- reading the whole
-    # accumulated log would see the previous attempt's line during a silent attempt
-    # and stop early. Guards the _mark slicing (three sites) directly.
-    cmd = _fake_cmd(
-        tmp_path, "slice.sh", 'case "$n" in 1|3|5) echo "ValueError: boom" >> "$LOGFILE";; esac\nexit 1'
-    )
-    r = _run(
-        tmp_path, cmd, _env(tmp_path, log, STARTUP_MAX_ATTEMPTS=5, STARTUP_REPEAT_LIMIT=2)
-    )
-    assert _exit_code(r.stdout) == 1
-    assert _runs(tmp_path) == 5  # never two same-class in a row -> whole budget
-
 
 # --- the acceptance gate: default flags emit a byte-identical script ---------
 
@@ -316,8 +60,8 @@ def _slurm_args(**over):
         slurm_time="4:00:00",
         array_size=5,
         use_wandb=False,
+        experiment_name="myexp",
         startup_max_attempts=1,
-        startup_stall_min=0,
         startup_repeat_limit=3,
         startup_retry_backoff_sec=15,
     )
@@ -329,28 +73,64 @@ def test_default_flags_emit_byte_identical_script():
     script, _ = ts.generate_slurm_script(
         _slurm_args(), "/exp/exp-TS", "JOB_CMD_PLACEHOLDER", "/img.sqsh", "jobname"
     )
-    assert "run_with_startup_retry" not in script
+    assert "run_with_retry" not in script
+    assert "retry_lib.sh" not in script
     assert script == GOLDEN_DEFAULT_SCRIPT
 
 
 def test_site_default_startup_knobs_keep_the_feature_off():
     # The byte-identical guarantee holds only if the RESOLVED default is off. The
-    # golden test above passes hardcoded args; guard the SITE_DEFAULTS values too so
-    # a change that flips the shipped default (e.g. startup_max_attempts: 2) -- which
-    # would wrap srun on a plain submission -- is caught here.
+    # golden test above passes hardcoded args; guard the SITE_DEFAULTS value too so a
+    # change that flips the shipped default (e.g. startup_max_attempts: 2) -- which
+    # would wrap srun on a plain submission -- is caught here. startup_stall_min is
+    # gone (one watchdog now), so assert it is not silently reintroduced.
     assert ts.SITE_DEFAULTS["startup_max_attempts"] == 1
-    assert ts.SITE_DEFAULTS["startup_stall_min"] == 0
+    assert "startup_stall_min" not in ts.SITE_DEFAULTS
 
 
-def test_feature_on_wraps_srun_and_exports_the_knobs():
+def test_feature_on_sources_the_lib_wraps_srun_and_exports_the_knobs():
     script, _ = ts.generate_slurm_script(
-        _slurm_args(startup_max_attempts=8, startup_stall_min=12),
+        _slurm_args(startup_max_attempts=8, startup_repeat_limit=5, startup_retry_backoff_sec=20),
         "/exp/exp-TS",
         "JOB_CMD_PLACEHOLDER",
         "/img.sqsh",
         "jobname",
     )
-    assert "run_with_startup_retry stdbuf -oL -eL srun" in script
-    assert "STARTUP_MAX_ATTEMPTS=8" in script
-    assert "STARTUP_STALL_SEC=720" in script  # 12 minutes -> seconds
-    assert "kill_tree()" in script  # its dependency is emitted too
+    # The batch layer sources the SHARED lib (not an inlined copy) and wraps srun.
+    assert "source /exp/exp-TS/scripts/cluster/retry_lib.sh" in script
+    assert "run_with_retry stdbuf -oL -eL srun" in script
+    # SLURM hands off to the array on progress rather than resuming in place.
+    assert "RETRY_ON_PROGRESS=break" in script
+    # The checkpoint the loop watches for progress is results/<experiment_name>.
+    assert "CKPT=/exp/exp-TS/results/myexp/last.ckpt" in script
+    assert "RETRY_MAX_ATTEMPTS=8" in script
+    assert "RETRY_REPEAT_LIMIT=5" in script
+    assert "RETRY_BACKOFF_SEC=20" in script
+
+
+def test_build_job_command_sources_the_lib_and_wraps_the_watchdog():
+    # The always-on inner watchdog is the ONE watchdog: build_job_command sources
+    # retry_lib.sh (after its cd into the snapshot) and runs training under
+    # run_with_stall_watchdog. No startup-only watchdog is emitted anywhere.
+    args = _slurm_args(
+        simulator="newton",
+        robot_name="g1",
+        motion_file="/m.pt",
+        num_envs=4096,
+        batch_size=32768,
+        experiment_path="examples/experiments/mimic/mlp.py",
+        training_max_iterations=None,
+        training_max_steps=100,
+        scenes_file=None,
+        checkpoint=None,
+        overrides=[],
+        stall_timeout_min=30,
+        seed=0,
+        torch_deterministic=False,
+        user="tester",
+        wandb_project="p",
+    )
+    job_cmd = ts.build_job_command(args, "/exp/exp-TS", "python")
+    assert "source /exp/exp-TS/scripts/cluster/retry_lib.sh" in job_cmd
+    assert "run_with_stall_watchdog python -u protomotions/train_agent.py" in job_cmd
+    assert "STALL_TIMEOUT_MIN=30" in job_cmd
diff --git a/protomotions/tests/test_train_slurm_watchdog.py b/protomotions/tests/test_train_slurm_watchdog.py
index a6014b2d1..3d50be44e 100644
--- a/protomotions/tests/test_train_slurm_watchdog.py
+++ b/protomotions/tests/test_train_slurm_watchdog.py
@@ -15,6 +15,7 @@
 import importlib.util
 import os
 import shutil
+import shlex
 import subprocess
 import sys
 from pathlib import Path
@@ -37,7 +38,14 @@ def _load_module():
 ts = _load_module()
 
 # The bash under test: the two function definitions, sourced into every snippet.
-WATCHDOG_BASH = ts._kill_tree_bash() + "\n" + ts._stall_watchdog_bash()
+if hasattr(ts, "_retry_lib_source"):
+    # The development launcher sources shared primitives; the public launcher
+    # embeds them. Exercise the implementation used by the loaded launcher.
+    WATCHDOG_BASH = ts._retry_lib_source(
+        shlex.quote(str(TRAIN_SLURM_PATH.parents[1]))
+    )
+else:
+    WATCHDOG_BASH = ts._kill_tree_bash() + "\n" + ts._stall_watchdog_bash()
 
 pytestmark = pytest.mark.skipif(
     sys.platform != "linux"
diff --git a/protomotions/train_agent.py b/protomotions/train_agent.py
index 13966a679..345a2b46c 100644
--- a/protomotions/train_agent.py
+++ b/protomotions/train_agent.py
@@ -64,20 +64,26 @@
 
 Example
 -------
->>> # Training with custom configuration
->>> # PYTHON_PATH protomotions/train_agent.py \\
->>> #     +exp=full_body_tracker/transformer_flat_terrain \\
->>> #     +robot=smpl \\
->>> #     +simulator=isaacgym \\
->>> #     motion_file=data/motions/amass_train.pt \\
->>> #     num_envs=2048 \\
->>> #     agent.config.batch_size=4096 \\
->>> #     +experiment_name=my_tracker
+Training with config overrides. An override path is
+``<config_type>.<field>[.<subfield>]``, where config_type is one of env,
+simulator, robot, agent, terrain, motion_lib, scene_lib. Note there is NO
+``.config`` level -- the fields sit directly on the config object, so
+``agent.num_mini_epochs=2`` is right and ``agent.config.num_mini_epochs=2``
+raises "Field 'config' not found"::
+
+    python protomotions/train_agent.py \\
+        --robot-name g1 --simulator isaaclab \\
+        --experiment-path examples/experiments/mimic/mlp.py \\
+        --experiment-name my_tracker \\
+        --motion-file data/motion_for_trackers/g1_bones_seed_mini.pt \\
+        --num-envs 2048 --batch-size 4096 \\
+        --overrides agent.num_mini_epochs=2 env.max_episode_length=1000
 """
 
 import os
 import sys
 import json
+import traceback
 
 os.environ["WANDB_DISABLE_SENTRY"] = "true"  # Must be first environment variable
 os.environ["WANDB_SILENT"] = "true"
@@ -225,6 +231,22 @@ def create_parser():
         default="physical_animation",
         help="Weights & Biases project name",
     )
+    parser.add_argument(
+        "--kit-thread-count",
+        type=int,
+        default=None,
+        help=(
+            "Pin Kit's task scheduler to this many threads (IsaacLab only). Default "
+            "None passes nothing: Kit keeps its own thread count on a single GPU, and "
+            "AppLauncher sets os.cpu_count() // WORLD_SIZE on multi-GPU runs. Set to 1 "
+            "to work around the IsaacLab 3 heap corruption during the first "
+            "sim.reset(), which scales with the number of distinct spawn prototypes: "
+            "measured 7/10 glibc aborts at the default vs 0/10 at 1, on 512 "
+            "prototypes / 8000 envs. The cost is small: ~68 s/epoch with the pin vs "
+            "~70 s/epoch without on 4xA100 with 4096 envs and 4500 furniture scenes. "
+            "On resume, a value given on the command line overrides the saved one."
+        ),
+    )
     parser.add_argument(
         "--use-slurm",
         action="store_true",
@@ -349,10 +371,23 @@ def detect_checkpoint_mode(args, save_dir):
             with open(checkpoint_config_path, "r") as file:
                 checkpoint_config = json.load(file)
 
-            # Update args with checkpoint config
+            # Update args with checkpoint config. --kit-thread-count is a launch-time
+            # workaround for a startup crash, not a training setting, and adding it to
+            # the resume command is how a run that starts aborting gets fixed; so a
+            # value given on this command line wins over the saved one, which for any
+            # run started without the flag is None.
+            cli_kit_thread_count = getattr(args, "kit_thread_count", None)
             for key, value in checkpoint_config.items():
                 if key != "wandb_id":
                     setattr(args, key, value)
+            if cli_kit_thread_count is not None:
+                saved = checkpoint_config.get("kit_thread_count")
+                if saved != cli_kit_thread_count:
+                    log.info(
+                        f"RESUME: using --kit-thread-count {cli_kit_thread_count} from "
+                        f"the command line instead of the saved value {saved}"
+                    )
+                args.kit_thread_count = cli_kit_thread_count
             wandb_id = checkpoint_config.get("wandb_id", None)
         else:
             raise FileNotFoundError(
@@ -825,6 +860,22 @@ def main():
             app_launcher_flags["distributed"] = True
             os.environ["LOCAL_RANK"] = str(fabric.local_rank)
             os.environ["RANK"] = str(fabric.global_rank)
+        # MUST go through kit_args, NOT sys.argv.append(). AppLauncher appends its
+        # OWN threadCount (os.cpu_count() // WORLD_SIZE) inside __init__, but only on
+        # the distributed branch (app_launcher.py:1034-1064) -- so a single-GPU run
+        # never sees it and a multi-GPU run silently loses our value. Kit takes the
+        # LAST occurrence of a duplicated setting (verified: appending 1 then 16
+        # aborts 2/2, appending 16 then 1 is clean), and _config_resolution calls
+        # _resolve_device_settings BEFORE _resolve_kit_args, whose `sys.argv +=
+        # self._kit_args` (app_launcher.py:1205) therefore lands last and wins.
+        # getattr, not attribute access: `args` does not always come from this
+        # parser (tests and other callers pass a plain namespace without the flag).
+        kit_thread_count = getattr(args, "kit_thread_count", None)
+        if kit_thread_count is not None:
+            kit_arg = f"--/plugins/carb.tasking.plugin/threadCount={kit_thread_count}"
+            existing = app_launcher_flags.get("kit_args", "")
+            app_launcher_flags["kit_args"] = f"{existing} {kit_arg}".strip()
+
         app_launcher = AppLauncher(app_launcher_flags)
         simulator_extra_params["simulation_app"] = app_launcher.app
 
@@ -1135,5 +1186,46 @@ def _handle_create_config_only(
     )
 
 
+def _run_main_reporting_failures() -> None:
+    """Run main(), making sure a crash actually exits non-zero.
+
+    Isaac Sim and IsaacLab each register an atexit hook (isaacsim
+    simulation_app.py, isaaclab app_launcher.py) that calls app.close() with the
+    default exit_code=0. Under Kit's fastShutdown that reaches
+    quickReleaseFrameworkAndTerminate, documented as calling _exit() on Linux --
+    so it replaces CPython's pending status 1 with 0 during interpreter
+    finalization. NVIDIA documents this: close(exit_code=...) exists precisely to
+    "preserve nonzero script/test failure status when fast shutdown is enabled".
+
+    The effect is that a crashed training run reports SUCCESS. Observed on a real
+    4-rank job where every rank raised torch.OutOfMemoryError and the retry loop
+    still logged "attempt 3 exited 0 -- done" and the cluster marked the job
+    Completed. Every retry loop, give-up sentinel and job state keyed on this exit
+    code was reading a value that could not report failure.
+
+    os._exit skips atexit entirely, so we set the status before Kit can overwrite
+    it. Only the failure path does this: on success we return normally and let the
+    hooks run, which is the clean shutdown they are there for.
+    """
+    try:
+        main()
+    except SystemExit as exit_request:
+        # Preserve an intentional exit code (argparse inside main(), for example)
+        # rather than turning a deliberate exit 0 into a failure.
+        code = exit_request.code
+        if code is None:
+            code = 0
+        elif not isinstance(code, int):
+            code = 1
+        sys.stdout.flush()
+        sys.stderr.flush()
+        os._exit(code)
+    except BaseException:
+        traceback.print_exc()
+        sys.stdout.flush()
+        sys.stderr.flush()
+        os._exit(1)
+
+
 if __name__ == "__main__":
-    main()
+    _run_main_reporting_failures()
diff --git a/protomotions/train_lepton.py b/protomotions/train_lepton.py
index 771d148ee..de75218b5 100644
--- a/protomotions/train_lepton.py
+++ b/protomotions/train_lepton.py
@@ -71,7 +71,10 @@
 # install. Same standalone invocation train_slurm.py supports.
 sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
 
-from protomotions._train_launch_common import build_train_agent_argv  # noqa: E402
+from protomotions._train_launch_common import (  # noqa: E402
+    RSYNC_EXCLUDES,
+    build_train_agent_argv,
+)
 
 
 # =============================================================================
@@ -306,6 +309,14 @@ def create_parser():
         "scene build that aborts several times before epoch 0; the default 3 kills "
         "a Lepton job after three ordinary aborts.",
     )
+    parser.add_argument(
+        "--repeat-limit",
+        type=int,
+        default=3,
+        help="Give up sooner if the SAME Python exception repeats this many "
+        "restarts in a row -- a deterministic bug, not a flaky abort. 0 disables "
+        "the check (fall back to --no-progress-limit alone). Default 3.",
+    )
     parser.add_argument(
         "--stall-timeout-min",
         type=int,
@@ -383,25 +394,6 @@ def teleport_host(site, user):
     return f"root@{site['workspace_id']}-{site['pod_name']}.{site['teleport_cluster']}"
 
 
-# rsync excludes: skip VCS, caches, and the large/derived data that must be staged
-# to AMLFS separately (mirrors train_slurm.py's list). A small motion file under
-# data/motion_for_trackers and the robot assets under protomotions/data DO travel.
-RSYNC_EXCLUDES = [
-    ".git",
-    ".idea",
-    "**/__pycache__",
-    "**/*.egg-info",
-    "outputs",
-    "output",
-    "results",
-    "exps",
-    "tmp",
-    "wandb",
-    "data/smpl",
-    "data/amass",
-    "data/pretrained_models",
-    "docs",
-]
 
 
 def stage_code(host, exp_folder, local_repo, execute):
@@ -510,7 +502,9 @@ def build_lep_job_create(args, site, exp_folder, container_command):
         "--env",
         f"STALL_TIMEOUT_MIN={args.stall_timeout_min}",
         "--env",
-        f"NO_PROGRESS_LIMIT={args.no_progress_limit}",
+        f"RETRY_MAX_ATTEMPTS={args.no_progress_limit}",
+        "--env",
+        f"RETRY_REPEAT_LIMIT={args.repeat_limit}",
         "--shared-memory-size",
         str(args.shared_memory_mib),
         # The in-job loop handles crashes within a running job. Job-level retry
diff --git a/protomotions/train_slurm.py b/protomotions/train_slurm.py
index b150b47a9..af95ec17e 100644
--- a/protomotions/train_slurm.py
+++ b/protomotions/train_slurm.py
@@ -102,7 +102,10 @@
 # install.
 sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
 
-from protomotions._train_launch_common import build_train_agent_args  # noqa: E402
+from protomotions._train_launch_common import (  # noqa: E402
+    RSYNC_EXCLUDES,
+    build_train_agent_args,
+)
 
 
 # =============================================================================
@@ -150,12 +153,16 @@
     # workflow/checkout-specific junk (personal side-project dirs, pre-staged data
     # directories, etc.) that should not ship to the cluster on every submit.
     "rsync_excludes": [],
-    # Opt-in startup retry + startup watchdog (see the --startup-* flags). These
-    # default to today's behaviour: no retry loop, no startup watchdog. They live
-    # here so a team can set a cluster-wide default in the site file -- the loader
-    # rejects unknown keys, so a site file could not set them without these entries.
+    # Opt-in startup retry (see the --startup-* flags). Defaults to today's
+    # behaviour: no retry loop. When on, the batch layer wraps srun in the shared
+    # run_with_retry (scripts/cluster/retry_lib.sh), which re-runs a scene build
+    # that aborts before training and fast-fails a repeated Python exception. A
+    # hung build is caught by the always-on stall watchdog inside srun (lower
+    # --stall-timeout-min for known-flaky builds), so there is no separate startup
+    # watchdog. These live here so a team can set a cluster-wide default in the site
+    # file -- the loader rejects unknown keys, so a site file could not set them
+    # without these entries.
     "startup_max_attempts": 1,
-    "startup_stall_min": 0,
     "startup_repeat_limit": 3,
     "startup_retry_backoff_sec": 15,
 }
@@ -517,27 +524,20 @@ def create_parser():
         "to --slurm-time, so total walltime is N x --slurm-time. --srun-repeat is "
         "a backward-compatible alias. Default: the site's array_size (else 5).",
     )
-    # Opt-in startup retry + startup watchdog. All default to None so the site
-    # file can supply a team-wide value (main() fills them like array_size); an
-    # explicit flag still wins. With the resolved defaults (1 attempt, 0 minutes)
-    # the emitted script is byte-identical to today's.
+    # Opt-in startup retry. All default to None so the site file can supply a
+    # team-wide value (main() fills them like array_size); an explicit flag still
+    # wins. With the resolved default (1 attempt) the emitted script is
+    # byte-identical to the no-retry path.
     parser.add_argument(
         "--startup-max-attempts",
         type=int,
         default=None,
         help="In-place srun attempts per array element, for scene-build failures "
-        "that abort or hang BEFORE epoch 0 (no training progress at stake). 1 (the "
-        "default) emits no retry loop at all. Retries reuse the same allocation "
-        "instead of costing a fresh array-element scheduling round trip.",
-    )
-    parser.add_argument(
-        "--startup-stall-min",
-        type=int,
-        default=None,
-        help="Outer startup watchdog, in minutes: kill and retry an attempt whose "
-        "log goes silent for this long BEFORE training starts (a wedged scene "
-        "build). 0 (default) disables it. Should be LESS than --stall-timeout-min "
-        "so it disarms before the inner watchdog would fire.",
+        "that abort BEFORE epoch 0 (no training progress at stake). 1 (the default) "
+        "emits no retry loop at all. Retries reuse the same allocation instead of "
+        "costing a fresh array-element scheduling round trip. A hung (not aborted) "
+        "scene build is killed by the always-on stall watchdog inside srun -- lower "
+        "--stall-timeout-min for known-flaky builds -- which then feeds this loop.",
     )
     parser.add_argument(
         "--startup-repeat-limit",
@@ -566,41 +566,7 @@ def sync_code_to_cluster(user, exp_folder, local_repo, extra_excludes=None):
     `extra_excludes` (from the site file's rsync_excludes) is appended to the
     built-in list, for workflow-specific junk and pre-staged data directories.
     """
-    exclude_patterns = [
-        ".git",
-        ".idea",
-        ".claude",
-        ".cursor",
-        ".vscode",
-        ".ruff_cache",
-        "**/__pycache__",
-        "**/*.egg-info",
-        # Local worktrees and in-tree virtualenvs are full duplicate checkouts /
-        # gigabytes -- never ship them.
-        ".worktrees",
-        ".venv**",
-        "outputs",
-        "output/*",
-        "results",
-        "exps",
-        "tmp",
-        "**/*tmp*",
-        # Root-level scratch caches (e.g. token/plan caches).
-        "**/_*_cache.pt",
-        # Large data blobs, pre-staged on the cluster rather than re-uploaded.
-        "data/smpl",
-        "data/amass",
-        "data/pretrained_models",
-        "docs",
-        "docs/*",
-        # Media that never belongs in a code snapshot.
-        "**/*.mp4",
-        "**/*.avi",
-        "**/*.gif",
-        "**/*.jpg",
-        "**/*.png",
-        "**/*.onnx",
-    ]
+    exclude_patterns = list(RSYNC_EXCLUDES)
     if extra_excludes:
         exclude_patterns += list(extra_excludes)
     exclude_str = " ".join([f'--exclude="{f}"' for f in exclude_patterns])
@@ -616,139 +582,14 @@ def sync_code_to_cluster(user, exp_folder, local_repo, extra_excludes=None):
     subprocess_run(rsync_cmd, shell=True)
 
 
-def _kill_tree_bash() -> str:
-    """bash defining kill_tree(): kill a process and ALL its descendants, children
-    first. The training python and its ranks run several levels below the srun
-    task, so killing only $pid orphans them -- they keep the GPUs and would
-    collide with the immediate resume. Extracted so it can be unit-tested
-    (test_train_slurm_watchdog.py) against a real fake process tree."""
-    return (
-        "kill_tree() { local root=$1 child; "
-        'for child in $(pgrep -P "$root" 2>/dev/null); do kill_tree "$child"; done; '
-        'kill -9 "$root" 2>/dev/null; }'
-    )
-
-
-def _stall_watchdog_bash() -> str:
-    """bash defining run_with_stall_watchdog(): run "$@" and kill its whole process
-    tree if $LOGFILE stops growing for a while.
-
-    Knobs (env): STALL_TIMEOUT_MIN is the user-facing one, in minutes; the watchdog
-    kills after that many no-growth polls of STALL_POLL_SEC (default 60s), i.e.
-    minutes by default. STALL_QUIET_LIMIT overrides the poll count directly and
-    STALL_POLL_SEC the interval, so a test can drive it in fractions of a second;
-    with the defaults this is identical to the previous 'sleep 60; quiet >=
-    STALL_TIMEOUT_MIN'. A limit <= 0 disables the watchdog. Extracted (with the
-    knobs) so it can be unit-tested without SLURM."""
-    return (
-        'run_with_stall_watchdog() { "$@" & local pid=$!; '
-        'local poll="${STALL_POLL_SEC:-60}"; '
-        'local quiet_limit="${STALL_QUIET_LIMIT:-${STALL_TIMEOUT_MIN:-30}}"; '
-        'if [ "${quiet_limit}" -le 0 ]; then wait "$pid"; return $?; fi; '
-        "( last=-1; quiet=0; "
-        'while kill -0 "$pid" 2>/dev/null; do sleep "$poll"; '
-        'size=$(stat -c %s "$LOGFILE" 2>/dev/null || echo 0); '
-        'if [ "$size" != "$last" ]; then last=$size; quiet=0; '
-        "else quiet=$((quiet + 1)); fi; "
-        'if [ "$quiet" -ge "${quiet_limit}" ]; then '
-        'echo "[watchdog] no log growth for ${quiet_limit} poll(s) of ${poll}s; killing pid ${pid} and descendants" >&2; '
-        'kill_tree "$pid"; break; fi; '
-        "done ) & local wd=$!; "
-        'wait "$pid"; local status=$?; '
-        'kill "$wd" 2>/dev/null; wait "$wd" 2>/dev/null; return "$status"; }'
-    )
-
-
-def _startup_retry_bash() -> str:
-    """bash defining run_with_startup_retry(): run "$@" up to STARTUP_MAX_ATTEMPTS
-    times, retrying only attempts that failed BEFORE reaching training.
-
-    This is the OUTER, startup-only counterpart to the inner run_with_stall_watchdog
-    in build_job_command. The inner one lives inside srun and kills the training
-    process; a retry loop cannot (you cannot re-run srun from inside srun), so this
-    wraps srun at the batch layer and re-runs it in place on the same allocation
-    when a scene build aborts (glibc heap corruption -> SIGABRT) or hangs before
-    epoch 0. It disarms the moment training is reached -- the inner watchdog owns
-    liveness from there -- and it refuses to retry a DETERMINISTIC failure: a native
-    abort prints no Python traceback, so a repeating exception line means a real
-    bug, not a flaky one, and the loop stops and surfaces it.
-
-    Knobs (env): STARTUP_MAX_ATTEMPTS (1 = no retry), STARTUP_STALL_SEC (0 = no
-    startup watchdog), STARTUP_POLL_SEC (30), STARTUP_BACKOFF_SEC (15),
-    STARTUP_REPEAT_LIMIT (3, 0 = off), TRAINING_MARKER (default 'Epoch [0-9]'),
-    LOGFILE. Extracted, with the knobs, so a test can drive it in fractions of a
-    second (test_train_slurm_startup_retry.py). Depends on kill_tree from
-    _kill_tree_bash()."""
-    return r'''run_with_startup_retry() {
-  local max="${STARTUP_MAX_ATTEMPTS:-1}" stall_sec="${STARTUP_STALL_SEC:-0}"
-  local poll="${STARTUP_POLL_SEC:-30}" backoff="${STARTUP_BACKOFF_SEC:-15}"
-  local repeat_limit="${STARTUP_REPEAT_LIMIT:-3}" marker="${TRAINING_MARKER:-Epoch [0-9]}"
-  local attempt=0 status=0 prev_cls="" repeat=0 _mark pid wd _exc _cls
-  # Always run at least once: a 0/negative max (e.g. someone paired it with a
-  # startup watchdog) must not silently skip srun and report success.
-  [ "$max" -lt 1 ] && max=1
-  while [ "$attempt" -lt "$max" ]; do
-    attempt=$((attempt + 1))
-    # Read only THIS attempt's slice of the shared log: everything from line
-    # _mark+1 on. wc/tail/grep below all key off it.
-    if [ -f "$LOGFILE" ]; then _mark=$(wc -l < "$LOGFILE"); else _mark=0; fi
-    "$@" &
-    pid=$!
-    wd=""
-    if [ "$stall_sec" -gt 0 ]; then
-      # Startup watchdog: kill the attempt if the log's mtime stops advancing for
-      # stall_sec BEFORE training is reached. Disarms (breaks) once the slice shows
-      # the training marker -- the inner run_with_stall_watchdog takes over then.
-      ( armed=1
-        while kill -0 "$pid" 2>/dev/null; do
-          sleep "$poll"
-          if [ "$armed" = 1 ] && tail -n +$((_mark + 1)) "$LOGFILE" 2>/dev/null | grep -qE "$marker"; then
-            break
-          fi
-          now=$(date +%s); mt=$(stat -c %Y "$LOGFILE" 2>/dev/null || echo "$now")
-          if [ $((now - mt)) -ge "$stall_sec" ]; then
-            echo "[startup-watchdog] no log activity for ${stall_sec}s before training started; killing pid ${pid} and its tree so this attempt can retry" >&2
-            kill_tree "$pid"
-            break
-          fi
-        done ) &
-      wd=$!
-    fi
-    wait "$pid"; status=$?
-    if [ -n "$wd" ]; then kill "$wd" 2>/dev/null; wait "$wd" 2>/dev/null; fi
-
-    if [ "$status" -eq 0 ]; then break; fi
-    if tail -n +$((_mark + 1)) "$LOGFILE" 2>/dev/null | grep -qE "$marker"; then
-      echo "[startup-retry] attempt ${attempt} reached training then exited ${status}; not retrying in place (the array element resumes from checkpoint)." >&2
-      break
-    fi
-    # Repeat check: a native abort/hang prints no Python traceback, so retry it
-    # freely (bounded by max). A DETERMINISTIC bug prints the same exception every
-    # attempt -- stop and surface it rather than hide it behind retries. Match
-    # message-LESS exceptions too: a bare `assert` prints just "AssertionError"
-    # with no colon, and that is one of the commonest deterministic startup bugs.
-    # Compare only the exception CLASS (strip the message): a message that embeds a
-    # path, CUDA device id or rank varies between attempts, but the bug is the same.
-    _exc=$(tail -n +$((_mark + 1)) "$LOGFILE" 2>/dev/null | grep -oE '^[A-Za-z_][A-Za-z0-9_.]*(Error|Exception)(: .*)?$' | tail -1)
-    _cls="${_exc%%:*}"
-    if [ -n "$_exc" ] && [ "$_cls" = "$prev_cls" ]; then
-      repeat=$((repeat + 1))
-    elif [ -n "$_exc" ]; then
-      repeat=1
-    fi
-    prev_cls="$_cls"
-    if [ -n "$_exc" ] && [ "$repeat_limit" -gt 0 ] && [ "$repeat" -ge "$repeat_limit" ]; then
-      echo "[startup-retry] same exception ${repeat} attempts in a row -- this is deterministic, not flaky:" >&2
-      echo "[startup-retry]   ${_exc}" >&2
-      break
-    fi
-    if [ "$attempt" -lt "$max" ]; then
-      echo "[startup-retry] attempt ${attempt}/${max} failed (exit ${status}) before training; retrying in ${backoff}s." >&2
-      sleep "$backoff"
-    fi
-  done
-  return "$status"
-}'''
+def _retry_lib_source(exp_folder) -> str:
+    """bash sourcing the shared retry/watchdog primitives (kill_tree,
+    run_with_stall_watchdog, run_with_retry) from the staged snapshot. Both the
+    batch layer and the in-srun command source the SAME file, so their retry and
+    watchdog logic cannot drift from lepton_train_loop.sh's. Absolute path: the
+    batch layer never cds, and inside srun the snapshot is mounted at exp_folder
+    too. Unit-tested by sourcing the file directly (test_cluster_retry_lib.py)."""
+    return f"source {exp_folder}/scripts/cluster/retry_lib.sh"
 
 
 def build_job_command(args, exp_folder, python_path, passthrough_args=None):
@@ -861,14 +702,15 @@ def build_job_command(args, exp_folder, python_path, passthrough_args=None):
     # observed silence. That case is rarer (collectives usually couple the ranks)
     # and out of scope here. Kill after STALL_TIMEOUT_MIN minutes of no growth; the
     # failed array element then restarts on the next (possibly different) node. 0
-    # disables. The bash is double-quoted throughout so it is safe inside the
-    # single-quoted `bash -c '...'` that srun wraps job_cmd in.
+    # disables. kill_tree + run_with_stall_watchdog are sourced from the shared
+    # retry_lib.sh (after the `cd {exp_folder}` above put the snapshot on the path)
+    # so this and lepton_train_loop.sh run the same watchdog. `source PATH` adds no
+    # single quotes, so it is safe inside the single-quoted `bash -c '...'` srun
+    # wraps job_cmd in.
     log_file = f"{exp_folder}/slurm_output.log"
-    kill_tree_fn = _kill_tree_bash()
-    watchdog_fn = _stall_watchdog_bash()
     job_cmd += (
         f"export PYTHONUNBUFFERED=1; LOGFILE={log_file}; "
-        f"STALL_TIMEOUT_MIN={args.stall_timeout_min}; {kill_tree_fn}; {watchdog_fn}; "
+        f"STALL_TIMEOUT_MIN={args.stall_timeout_min}; {_retry_lib_source(exp_folder)}; "
         f"run_with_stall_watchdog {train_cmd}"
     )
 
@@ -903,22 +745,27 @@ def generate_slurm_script(args, exp_folder, job_cmd, container_image, job_name):
         f"/bin/bash -c '{job_cmd_quoted}'"
     )
 
-    # Opt-in startup retry. When off (the default), the body is the bare srun_cmd
-    # and this script is byte-identical to before the feature existed. When on,
-    # wrap srun in run_with_startup_retry, which re-runs it in place for a scene
-    # build that failed before training. srun_cmd is passed as ARGUMENTS (not
-    # re-quoted): it already contains a single-quoted bash -c '...' and must not be
-    # escaped again.
-    retry_on = args.startup_max_attempts > 1 or args.startup_stall_min > 0
+    # Opt-in startup retry. When off (the default), the body is the bare srun_cmd.
+    # When on, source the shared retry_lib.sh at the batch layer and wrap srun in
+    # run_with_retry, which re-runs a scene build that failed before training and
+    # fast-fails a repeated Python exception. RETRY_ON_PROGRESS=break: the moment a
+    # checkpoint is written (training reached), stop retrying in place -- the SLURM
+    # array's next task resumes from it. No GAVE_UP sentinel here: a deterministic
+    # scene-build bug fast-fails per array task (each is quick), and the array is the
+    # coarse bound. srun_cmd is passed as ARGUMENTS (not re-quoted): it already
+    # contains a single-quoted bash -c '...' and must not be escaped again.
+    retry_on = args.startup_max_attempts > 1
     if retry_on:
+        ckpt = f"{exp_folder}/results/{args.experiment_name}/last.ckpt"
         body = (
-            f"{_kill_tree_bash()}\n{_startup_retry_bash()}\n"
+            f"{_retry_lib_source(exp_folder)}\n"
             f"export LOGFILE={log_file} "
-            f"STARTUP_MAX_ATTEMPTS={args.startup_max_attempts} "
-            f"STARTUP_STALL_SEC={args.startup_stall_min * 60} "
-            f"STARTUP_BACKOFF_SEC={args.startup_retry_backoff_sec} "
-            f"STARTUP_REPEAT_LIMIT={args.startup_repeat_limit}\n"
-            f"run_with_startup_retry {srun_cmd}"
+            f"CKPT={ckpt} "
+            f"RETRY_MAX_ATTEMPTS={args.startup_max_attempts} "
+            f"RETRY_REPEAT_LIMIT={args.startup_repeat_limit} "
+            f"RETRY_BACKOFF_SEC={args.startup_retry_backoff_sec} "
+            f"RETRY_ON_PROGRESS=break\n"
+            f"run_with_retry {srun_cmd}"
         )
     else:
         body = srun_cmd
@@ -990,12 +837,11 @@ def main():
     # once) when neither --array-size nor its --srun-repeat alias was given.
     if args.array_size is None:
         args.array_size = site.get("array_size", 5)
-    # The startup-retry / startup-watchdog knobs resolve the same way: a flag left
-    # None takes the site value (which defaults to the off values in SITE_DEFAULTS),
-    # and an explicit flag still wins.
+    # The startup-retry knobs resolve the same way: a flag left None takes the site
+    # value (which defaults to the off value in SITE_DEFAULTS), and an explicit flag
+    # still wins.
     for _key, _default in (
         ("startup_max_attempts", 1),
-        ("startup_stall_min", 0),
         ("startup_repeat_limit", 3),
         ("startup_retry_backoff_sec", 15),
     ):
@@ -1022,24 +868,13 @@ def main():
         )
 
     # Startup retry multiplies with the job array. Print the worst-case execution
-    # count before anything is submitted, and warn if the startup watchdog is set
-    # so high the inner stall watchdog would always fire first (leaving it inert).
-    if args.startup_max_attempts > 1 or args.startup_stall_min > 0:
+    # count before anything is submitted.
+    if args.startup_max_attempts > 1:
         worst = args.startup_max_attempts * (args.array_size + 1)
         print(
             f"[startup-retry] up to {args.startup_max_attempts} attempts x "
             f"{args.array_size + 1} array elements = {worst} executions worst case"
         )
-        if (
-            args.startup_stall_min > 0
-            and args.stall_timeout_min > 0
-            and args.startup_stall_min >= args.stall_timeout_min
-        ):
-            print(
-                f"[startup-retry] WARNING: --startup-stall-min ({args.startup_stall_min}) "
-                f">= --stall-timeout-min ({args.stall_timeout_min}); the inner watchdog "
-                "fires first, so the startup watchdog never acts. Set it lower."
-            )
 
     # Setup paths
     timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
@@ -1075,10 +910,16 @@ def main():
     # Build job command
     job_cmd = build_job_command(args, exp_folder, python_path, passthrough_args)
 
-    # SLURM job name carries the timestamp so a squeue row maps to its exp-<ts>
+    # SLURM job name carries the submit TIME so a squeue row maps to its exp-<ts>
     # directory and its output log -- otherwise squeue tells you nothing about
     # where a job's output lives.
-    job_name = f"{timestamp}_{args.experiment_name}"
+    #
+    # Time only, not the full timestamp: squeue's NAME column is 8 characters, so
+    # a leading date would fill it entirely and every job submitted on the same
+    # day would render identically. The date is recoverable from the job's submit
+    # date, and the exp-<slurm_job_id> symlink created after submission below
+    # gives the exact directory anyway.
+    job_name = f"{timestamp.split('_')[1]}_{args.experiment_name}"
     slurm_script, log_file = generate_slurm_script(
         args, exp_folder, job_cmd, container_image, job_name
     )
@@ -1116,6 +957,33 @@ def main():
             slurm_job_id = line.split()[-1].strip()
             break
 
+    # Make the exp directory addressable by SLURM job id, so squeue's JOBID column
+    # leads straight to a path. For an array job sbatch returns the base id, which
+    # is the part squeue prints before the "_<task>" suffix, so exp-<base id> is
+    # what you can actually copy off a squeue row.
+    #
+    # A symlink rather than a rename: the sbatch script, --output and --error all
+    # have the exp-<ts> path baked in already, and the job may be pending or
+    # running by now. Failure here is not worth aborting a submitted job over, so
+    # it is best-effort and only reported.
+    exp_link = None
+    if slurm_job_id != "unknown":
+        candidate = os.path.join(os.path.dirname(exp_folder), f"exp-{slurm_job_id}")
+        link_result = subprocess_run(
+            f"ssh {args.user}@{CLUSTER_LOGIN_NODE} 'ln -sfn {exp_folder} {candidate}'",
+            shell=True,
+            capture_output=True,
+            text=True,
+            ignore_err=True,
+        )
+        if link_result.returncode == 0:
+            exp_link = candidate
+        else:
+            print(
+                f"warning: could not link exp-{slurm_job_id} -> {exp_folder} "
+                f"({(link_result.stderr or '').strip()})"
+            )
+
     # Copy-back hint: auto-discover the helper wherever it lives (it has moved
     # between directories). rglob avoids naming any specific path this file must
     # not reference; first non-worktree match wins.
@@ -1145,6 +1013,8 @@ def main():
     print(
         f"Cancel job:    ssh {args.user}@{CLUSTER_LOGIN_NODE} 'scancel {slurm_job_id}'"
     )
+    if exp_link:
+        print(f"Exp by job id: {exp_link}")
     if copy_back_cmd:
         print(f"Copy back:     {copy_back_cmd}")
     print("=" * 60)
@@ -1155,6 +1025,8 @@ def main():
     print(f"slurm_job_id={slurm_job_id}")
     print(f"exp_id={timestamp}")
     print(f"exp_dir={exp_folder}")
+    if exp_link:
+        print(f"exp_link={exp_link}")
     print(f"output_log={log_file}")
     if copy_back_cmd:
         print(f"copy_back={copy_back_cmd}")
diff --git a/protomotions/utils/retargeting_keypoints.py b/protomotions/utils/retargeting_keypoints.py
new file mode 100644
index 000000000..649a1ef9f
--- /dev/null
+++ b/protomotions/utils/retargeting_keypoints.py
@@ -0,0 +1,65 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Conventions of the retargeting keypoint files.
+
+data/scripts/keypoint_utils.py writes the files and the PyRoki retargeters
+(pyroki/batch_retarget_to_*_from_keypoints.py) read them. Keypoint 0 is the
+pelvis, 1 and 2 the left and right hip joints.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+
+# Where keypoint_utils stores the SOMA pelvis keypoint, in the Hips joint frame
+# (whose -y is forward): 7 cm behind the joint. The value was carried over from
+# the Bones v24 skeleton, whose spine leaves the hips going backward. Robots match
+# the pelvis keypoint with their pelvis origin, so the retargeters move it back
+# onto the joint (soma_pelvis_keypoint_on_hips); keypoint files already on disk
+# all carry the shift.
+SOMA_PELVIS_KEYPOINT_SHIFT = (0.0, 0.07, 0.0)
+
+# Forward position of the midpoint of the two SOMA hip joints, in the Hips frame:
+# 2.6 cm in front of the Hips joint (soma23_humanoid.xml). It identifies which
+# convention a keypoint file uses.
+_SOMA_HIP_MIDPOINT_Y = -0.026
+_TOLERANCE = 0.015
+
+
+def _hip_midpoint_y(positions: np.ndarray, orientations: np.ndarray) -> float:
+    """Median forward position of the hip midpoint in the pelvis keypoint frame."""
+    hip_mid = 0.5 * (positions[:, 1] + positions[:, 2]) - positions[:, 0]
+    local = np.einsum("tji,tj->ti", orientations[:, 0], hip_mid)
+    return float(np.median(local[:, 1]))
+
+
+def soma_pelvis_keypoint_on_hips(
+    positions: np.ndarray, orientations: np.ndarray
+) -> np.ndarray:
+    """SOMA keypoint positions with the pelvis keypoint on the Hips joint.
+
+    Files written with the shift are moved back by it; files whose pelvis
+    keypoint is already on the joint are returned unchanged. Anything else (for
+    example keypoints of another skeleton) raises ValueError. Only keypoint 0
+    moves: the pelvis aux point keeps its stored position, 15.5 cm in front of
+    the joint.
+
+    Args:
+        positions: [T, K, 3] keypoint positions.
+        orientations: [T, K, 3, 3] keypoint orientations (keypoint 0 = Hips).
+    """
+    shift = np.asarray(SOMA_PELVIS_KEYPOINT_SHIFT, dtype=positions.dtype)
+    hip_mid_y = _hip_midpoint_y(positions, orientations)
+    if abs(hip_mid_y - (_SOMA_HIP_MIDPOINT_Y - shift[1])) < _TOLERANCE:
+        positions = positions.copy()
+        positions[:, 0] -= np.einsum("tij,j->ti", orientations[:, 0], shift)
+        return positions
+    if abs(hip_mid_y - _SOMA_HIP_MIDPOINT_Y) < _TOLERANCE:
+        return positions
+    raise ValueError(
+        "Not SOMA keypoints: the hip joints are "
+        f"{-hip_mid_y * 100:.1f} cm in front of the pelvis keypoint; expected "
+        f"{(shift[1] - _SOMA_HIP_MIDPOINT_Y) * 100:.1f} (extracted with the pelvis "
+        f"shift) or {-_SOMA_HIP_MIDPOINT_Y * 100:.1f} (without it)."
+    )
diff --git a/pyroki/batch_retarget_to_g1_from_keypoints.py b/pyroki/batch_retarget_to_g1_from_keypoints.py
index 34a9825bb..95916e1ec 100644
--- a/pyroki/batch_retarget_to_g1_from_keypoints.py
+++ b/pyroki/batch_retarget_to_g1_from_keypoints.py
@@ -23,11 +23,45 @@
     sys.path.insert(0, str(REPO_ROOT))
 
 from protomotions.utils.retargeting_fps import fps_from_mapping, subsampled_fps  # noqa: E402
+from protomotions.utils.retargeting_keypoints import soma_pelvis_keypoint_on_hips  # noqa: E402
 
 G1_LINK_NAMES = None
 N_retarget = 15
 N_AUX = 3
 
+# --- Per-source settings, keyed by --source-type ------------------------------
+
+# Rotation taking the source pelvis frame to the robot pelvis frame. It is only
+# used to initialise the solver's root orientation. SOMA's Hips faces its local
+# -y (its +x is the character's left), so the robot, which faces +x, needs a
+# -90 deg turn about z; SMPL's pelvis already faces +x.
+SOURCE_ROOT_TO_ROBOT_ROOT = {
+    "soma": ((0.0, 1.0, 0.0), (-1.0, 0.0, 0.0), (0.0, 0.0, 1.0)),
+    "smpl": ((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0)),
+}
+
+# Joints held near their rest angle with the joint_rest_penalty weight; every
+# other joint gets 0.02. Waist pitch and pelvis pitch both tilt the torso. With
+# only the small weight on waist pitch, the solver put all of a SOMA source's
+# forward lean into the waist: it sat at its 0.52 rad stop in ~90% of frames,
+# with the pelvis tipped back against the source's. Holding it moves the lean
+# back to the pelvis. On SMPL sources holding it was not a clear win (the pelvis
+# then tipped ~11 deg further forward than the source's and the knees bent
+# more), so SMPL keeps the original set.
+JOINTS_TO_MOVE_LESS = {
+    "soma": [
+        "waist_roll_joint",
+        "waist_pitch_joint",
+        "right_wrist_pitch_joint",
+        "left_wrist_pitch_joint",
+    ],
+    "smpl": [
+        "waist_roll_joint",
+        "right_wrist_pitch_joint",
+        "left_wrist_pitch_joint",
+    ],
+}
+
 # for the local bones alignment cost
 direct_pairs = [
     # ("pelvis", "left_shoulder", 1.0),
@@ -109,6 +143,10 @@ def load_motion_data(
 
     raw_positions = motion_data["positions"]
     raw_orientations = motion_data["orientations"]
+    if source_type == "soma":
+        # The robot matches the pelvis keypoint with its pelvis origin, so undo
+        # keypoint_utils' 7 cm shift of it behind the Hips joint.
+        raw_positions = soma_pelvis_keypoint_on_hips(raw_positions, raw_orientations)
     raw_left_foot_contacts = motion_data[
         "left_foot_contacts"
     ]  # [T, 2] - ankle, toebase
@@ -229,6 +267,12 @@ def apply_crossfade(contact_flags):
         )
 
     elif source_type == "soma":
+        # Pelvis and legs: horizontal 0.8 is the robot/source standing pelvis
+        # height (0.793 / 0.99) and vertical 0.807 the pelvis-to-ankle drop
+        # (0.757 / 0.938), so a straight-legged source keeps G1's legs straight.
+        # Knee bend is very sensitive to the vertical factor: near a straight
+        # leg, a pelvis target 1 cm too low bends G1's knee by ~20 deg. (The
+        # previous 0.75, the hip-joint height ratio, put it ~5 cm too low.)
         simplified_keypoints_root = simplified_keypoints[:, 0, :]
         simplified_keypoints_local = (
             simplified_keypoints - simplified_keypoints_root[:, None, :]
@@ -236,7 +280,7 @@ def apply_crossfade(contact_flags):
         simplified_keypoints_lower_body_local = simplified_keypoints_local[:, 1:9, :]
         simplified_keypoints_lower_body_local = (
             simplified_keypoints_lower_body_local
-            * onp.array([0.8, 0.8, 0.75])[None, None, :]
+            * onp.array([0.8, 0.8, 0.807])[None, None, :]
         )
 
         simplified_keypoints_upper_body_local = simplified_keypoints_local[
@@ -256,7 +300,7 @@ def apply_crossfade(contact_flags):
         )
 
         simplified_keypoints_root = (
-            simplified_keypoints_root * onp.array([0.8, 0.8, 0.75])[None, :]
+            simplified_keypoints_root * onp.array([0.8, 0.8, 0.807])[None, :]
         )
         simplified_keypoints = (
             simplified_keypoints_root[:, None, :] + simplified_keypoints_local
@@ -417,6 +461,7 @@ def main():
         "--source-type",
         type=str,
         default="smpl",
+        choices=sorted(SOURCE_ROOT_TO_ROBOT_ROOT),
         help="Source type for the retargeting.",
     )
     parser.add_argument(
@@ -604,6 +649,7 @@ def generate_trajectory():
                 weights=weights.get_weights(),  # type: ignore
                 subsample_factor=subsample_factor,
                 input_fps=input_fps,
+                source_type=args.source_type,
             )
             gen_button.disabled = False
             retarget_next_button.disabled = False  # Re-enable after generating
@@ -725,6 +771,7 @@ def retarget_next_motion(_: viser.GuiEvent):
                 weights=weights_dict,
                 subsample_factor=subsample_factor,
                 input_fps=input_fps,
+                source_type=args.source_type,
             )
 
             # Save results, sliced to the actual motion length
@@ -912,22 +959,24 @@ def solve_retargeting(
     weights: RetargetingWeights,
     subsample_factor: int = 1,
     input_fps: float = 30.0,
+    source_type: jdc.Static[str] = "smpl",
 ) -> Tuple[jaxlie.SE3, jnp.ndarray]:
-    """Solve the simplified retargeting problem."""
+    """Solve the simplified retargeting problem.
+
+    source_type selects the per-source settings above (it must match the
+    keypoint files).
+    """
 
     n_retarget = len(g1_joint_retarget_indices)
     timesteps = target_keypoints.shape[0]
+    source_root_to_robot_root = jnp.array(SOURCE_ROOT_TO_ROBOT_ROOT[source_type])
 
     # Robot properties.
     # - Joints that should move less for natural humanoid motion.
     joints_to_move_less = jnp.array(
         [
             robot.joints.actuated_names.index(name)
-            for name in [
-                "waist_roll_joint",
-                "right_wrist_pitch_joint",
-                "left_wrist_pitch_joint",
-            ]
+            for name in JOINTS_TO_MOVE_LESS[source_type]
         ]
     )
 
@@ -950,11 +999,14 @@ class SimplifiedJointsScaleVarG1(
     var_Ts_world_root = jaxls.SE3Var(jnp.arange(timesteps))
     var_joints_scale = SimplifiedJointsScaleVarG1(jnp.zeros(timesteps))
 
-    # Initialize root position and orientation using source root at each timestep t
+    # Initialize root position and orientation using source root at each timestep t,
+    # turned into the robot pelvis convention.
     root_init_se3_list = []
     for t in range(timesteps):
-        root_pos_t = target_keypoints[t, 0, :]  # Root position at timestep t
-        root_rot_t = target_orientations[t, 0, :, :]  # Root orientation at timestep t
+        # Root orientation at timestep t
+        root_rot_t = target_orientations[t, 0, :, :] @ source_root_to_robot_root
+        # Root position at timestep t
+        root_pos_t = target_keypoints[t, 0, :]
 
         # Create SE3 transformation for this timestep
         root_se3_t = jaxlie.SE3.from_rotation_and_translation(
@@ -1087,7 +1139,8 @@ def pc_alignment_cost(
         # we use torso here to prevent odd waist rotations
         # but then we need 0.07m height offset...
 
-        # x=0.18 matches source pelvis_aux (0.2 * 0.9x scaling = 0.18).
+        # x=0.18 matches the SMPL source pelvis_aux (0.2 * 0.9x scaling = 0.18);
+        # the SOMA one is 0.155 * 0.8 = 0.124 in front of the pelvis, so this leads it.
         # z=0.03 to encourage forward torso tilt; note source
         # pelvis_aux is at pelvis height which is even lower, also encourages forward torso tilt.
         torso_aux_pos = link_pos_torso + link_rot_mat_torso @ jnp.array(
diff --git a/pyroki/batch_retarget_to_h1_2_from_keypoints.py b/pyroki/batch_retarget_to_h1_2_from_keypoints.py
index df2f506cf..0c8b7320e 100644
--- a/pyroki/batch_retarget_to_h1_2_from_keypoints.py
+++ b/pyroki/batch_retarget_to_h1_2_from_keypoints.py
@@ -23,11 +23,23 @@
     sys.path.insert(0, str(REPO_ROOT))
 
 from protomotions.utils.retargeting_fps import fps_from_mapping, subsampled_fps  # noqa: E402
+from protomotions.utils.retargeting_keypoints import soma_pelvis_keypoint_on_hips  # noqa: E402
 
 H1_2_LINK_NAMES = None
 N_retarget = 15
 N_AUX = 3
 
+# --- Per-source settings, keyed by --source-type ------------------------------
+
+# Rotation taking the source pelvis frame to the robot pelvis frame. It is only
+# used to initialise the solver's root orientation. SOMA's Hips faces its local
+# -y (its +x is the character's left), so the robot, which faces +x, needs a
+# -90 deg turn about z; SMPL's pelvis already faces +x.
+SOURCE_ROOT_TO_ROBOT_ROOT = {
+    "soma": ((0.0, 1.0, 0.0), (-1.0, 0.0, 0.0), (0.0, 0.0, 1.0)),
+    "smpl": ((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0)),
+}
+
 # for the local bones alignment cost
 direct_pairs = [
     # ("pelvis", "left_shoulder", 1.0),
@@ -106,6 +118,10 @@ def load_motion_data(
 
     raw_positions = motion_data["positions"]
     raw_orientations = motion_data["orientations"]
+    if source_type == "soma":
+        # The robot matches the pelvis keypoint with its pelvis origin, so undo
+        # keypoint_utils' 7 cm shift of it behind the Hips joint.
+        raw_positions = soma_pelvis_keypoint_on_hips(raw_positions, raw_orientations)
     raw_left_foot_contacts = motion_data[
         "left_foot_contacts"
     ]  # [T, 2] - ankle, toebase
@@ -226,6 +242,12 @@ def apply_crossfade(contact_flags):
         )
 
     elif source_type == "soma":
+        # Pelvis and legs: vertical 1.048 is the robot/source pelvis-to-ankle
+        # drop (0.983 / 0.938), so a straight-legged source keeps H1_2's legs
+        # straight; horizontal 1.04 sits between that and the standing pelvis
+        # height ratio (1.02 / 0.99). Knee bend is very sensitive to the vertical
+        # factor near a straight leg; the previous 1.0 put the pelvis target
+        # ~4 cm too low and kept H1_2's knees bent.
         simplified_keypoints_root = simplified_keypoints[:, 0, :]
         simplified_keypoints_local = (
             simplified_keypoints - simplified_keypoints_root[:, None, :]
@@ -233,7 +255,7 @@ def apply_crossfade(contact_flags):
         simplified_keypoints_lower_body_local = simplified_keypoints_local[:, 1:9, :]
         simplified_keypoints_lower_body_local = (
             simplified_keypoints_lower_body_local
-            * onp.array([1.0, 1.0, 1.0])[None, None, :]
+            * onp.array([1.04, 1.04, 1.048])[None, None, :]
         )
 
         simplified_keypoints_upper_body_local = simplified_keypoints_local[
@@ -253,7 +275,7 @@ def apply_crossfade(contact_flags):
         )
 
         simplified_keypoints_root = (
-            simplified_keypoints_root * onp.array([1.0, 1.0, 1.0])[None, :]
+            simplified_keypoints_root * onp.array([1.04, 1.04, 1.048])[None, :]
         )
         simplified_keypoints = (
             simplified_keypoints_root[:, None, :] + simplified_keypoints_local
@@ -414,6 +436,7 @@ def main():
         "--source-type",
         type=str,
         default="smpl",
+        choices=sorted(SOURCE_ROOT_TO_ROBOT_ROOT),
         help="Source type for the retargeting.",
     )
     parser.add_argument(
@@ -601,6 +624,7 @@ def generate_trajectory():
                 weights=weights.get_weights(),  # type: ignore
                 subsample_factor=subsample_factor,
                 input_fps=input_fps,
+                source_type=args.source_type,
             )
             gen_button.disabled = False
             retarget_next_button.disabled = False  # Re-enable after generating
@@ -722,6 +746,7 @@ def retarget_next_motion(_: viser.GuiEvent):
                 weights=weights_dict,
                 subsample_factor=subsample_factor,
                 input_fps=input_fps,
+                source_type=args.source_type,
             )
 
             # Save results, sliced to the actual motion length
@@ -909,11 +934,17 @@ def solve_retargeting(
     weights: RetargetingWeights,
     subsample_factor: int = 1,
     input_fps: float = 30.0,
+    source_type: jdc.Static[str] = "smpl",
 ) -> Tuple[jaxlie.SE3, jnp.ndarray]:
-    """Solve the simplified retargeting problem."""
+    """Solve the simplified retargeting problem.
+
+    source_type selects the per-source settings above (it must match the
+    keypoint files).
+    """
 
     n_retarget = len(h1_2_joint_retarget_indices)
     timesteps = target_keypoints.shape[0]
+    source_root_to_robot_root = jnp.array(SOURCE_ROOT_TO_ROBOT_ROOT[source_type])
 
     # Robot properties.
     # - Joints that should move less for natural humanoid motion.
@@ -937,11 +968,14 @@ class SimplifiedJointsScaleVarH1_2(
     var_Ts_world_root = jaxls.SE3Var(jnp.arange(timesteps))
     var_joints_scale = SimplifiedJointsScaleVarH1_2(jnp.zeros(timesteps))
 
-    # Initialize root position and orientation using source root at each timestep t
+    # Initialize root position and orientation using source root at each timestep t,
+    # turned into the robot pelvis convention.
     root_init_se3_list = []
     for t in range(timesteps):
-        root_pos_t = target_keypoints[t, 0, :]  # Root position at timestep t
-        root_rot_t = target_orientations[t, 0, :, :]  # Root orientation at timestep t
+        # Root orientation at timestep t
+        root_rot_t = target_orientations[t, 0, :, :] @ source_root_to_robot_root
+        # Root position at timestep t
+        root_pos_t = target_keypoints[t, 0, :]
 
         # Create SE3 transformation for this timestep
         root_se3_t = jaxlie.SE3.from_rotation_and_translation(
diff --git a/scripts/check_model_licenses.py b/scripts/check_model_licenses.py
new file mode 100644
index 000000000..93177aeb9
--- /dev/null
+++ b/scripts/check_model_licenses.py
@@ -0,0 +1,104 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Check published model licensing without importing ML packages or LFS data."""
+
+import argparse
+import hashlib
+import json
+from pathlib import Path
+import re
+
+
+DEFAULT_LICENSE = "NVIDIA Open Model License"
+NOTICE = "Licensed by NVIDIA Corporation under the NVIDIA Open Model License"
+LICENSE_SHA256 = "4d2fb590aa9b30c47f2058bff17291df7fa2aa0c1bd775a20703da9bb267cfab"
+MODEL_SUFFIXES = {".ckpt", ".onnx", ".safetensors", ".pth", ".pt", ".engine", ".plan"}
+
+
+def model_directories(root):
+    """Find cards AND uncarded payloads, including export-only model releases."""
+    models = root / "data/pretrained_models"
+    cards = {p.parent for p in models.rglob("MODEL_CARD.md")}
+    directories = set(cards)
+    for path in models.rglob("*"):
+        if not path.is_file() or path.suffix.lower() not in MODEL_SUFFIXES:
+            continue
+        # Exported artifacts belong to their closest enclosing model card.
+        owner = next((p for p in path.parents if p in cards), path.parent)
+        directories.add(owner)
+    return sorted(directories)
+
+
+def check(root):
+    """Return actionable errors; approved exceptions never disable card checks."""
+    errors = []
+    canonical = root / "legal/NVIDIA-Open-Model-License.pdf"
+    if not canonical.is_file() or hashlib.sha256(canonical.read_bytes()).hexdigest() != LICENSE_SHA256:
+        errors.append("Missing or altered canonical NVIDIA Open Model License PDF")
+    manifest = root / "legal/model-license-exceptions.json"
+    try:
+        exceptions = json.loads(manifest.read_text())
+    except (OSError, ValueError) as error:
+        return errors + [f"{manifest}: {error}"]
+    if not isinstance(exceptions, dict):
+        return errors + ["Model license exceptions must be a JSON object"]
+    directories = model_directories(root)
+    if not directories:
+        errors.append("No published model directories found")
+    names = {p.relative_to(root).as_posix() for p in directories}
+    for stale in exceptions.keys() - names:
+        errors.append(f"Stale or unknown model license exception: {stale}")
+    for directory in directories:
+        name = directory.relative_to(root).as_posix()
+        license_name, filename, reason = DEFAULT_LICENSE, "LICENSE.pdf", None
+        if name in exceptions:
+            entry = exceptions[name]
+            if (not isinstance(entry, dict)
+                    or set(entry) != {"license", "license_file", "reason"}
+                    or not all(isinstance(v, str) and v.strip() for v in entry.values())):
+                errors.append(f"{name}: exception requires license, license_file and reason")
+                continue
+            license_name, filename, reason = entry["license"], entry["license_file"], entry["reason"]
+            if (license_name == DEFAULT_LICENSE or Path(filename).name != filename
+                    or filename in {".", "..", "MODEL_CARD.md", "NOTICE.txt"}
+                    or "\\" in filename):
+                errors.append(f"{name}: invalid exception license or filename")
+                continue
+        card = directory / "MODEL_CARD.md"
+        text = card.read_text() if card.is_file() else ""
+        sections = re.findall(r"^## License\s*\n(.*?)(?=^## |\Z)", text, re.M | re.S)
+        section = sections[0] if len(sections) == 1 else ""
+        if not re.search(r"^Model license: " + re.escape(license_name) + r"\s*$", section, re.M):
+            errors.append(f"{name}: card must declare 'Model license: {license_name}' in ## License")
+        if reason and f"License exception: {reason}" not in section:
+            errors.append(f"{name}: document the exception rationale in ## License")
+        for linked in (filename, "NOTICE.txt"):
+            if f"]({linked})" not in section:
+                errors.append(f"{name}: ## License must link {linked}")
+        license_path = directory / filename
+        if license_path.is_symlink() or not license_path.is_file() or not license_path.stat().st_size:
+            errors.append(f"{name}: bundle a regular, nonempty {filename}")
+        elif reason is None and hashlib.sha256(license_path.read_bytes()).hexdigest() != LICENSE_SHA256:
+            errors.append(f"{name}: {filename} must match the canonical NVIDIA license")
+        notice = directory / "NOTICE.txt"
+        if notice.is_symlink() or not notice.is_file() or not notice.read_text().strip():
+            errors.append(f"{name}: bundle a nonempty NOTICE.txt")
+        elif reason is None and NOTICE not in notice.read_text():
+            errors.append(f"{name}: NOTICE.txt is missing NVIDIA model attribution")
+    return errors
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--root", type=Path, default=Path(__file__).resolve().parents[1])
+    args = parser.parse_args()
+    errors = check(args.root.resolve())
+    for error in errors:
+        print(error)
+    if not errors:
+        print("Model license checks passed.")
+    return bool(errors)
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/cluster/lepton_train_loop.sh b/scripts/cluster/lepton_train_loop.sh
index aee3b027b..2c0586afc 100755
--- a/scripts/cluster/lepton_train_loop.sh
+++ b/scripts/cluster/lepton_train_loop.sh
@@ -12,31 +12,37 @@
 #
 # Resume is filesystem-based and needs no flags: train_agent.py finds
 # results/<exp>/last.ckpt in the (unchanged) snapshot cwd and continues. It saves
-# last.ckpt every K epochs (default 10) regardless of SLURM, which is what makes a
-# plain re-invocation safe. So each loop iteration runs the identical command; the
-# first run starts fresh, later runs auto-resume.
+# last.ckpt at loop entry (epoch 0) and every K epochs thereafter regardless of
+# SLURM, which is what makes a plain re-invocation safe. So each loop iteration
+# runs the identical command; the first run starts fresh, later runs auto-resume.
+#
+# The crash-restart policy itself (retry flaky failures, fast-fail a repeated
+# Python exception, key progress off last.ckpt's mtime) lives in the shared
+# run_with_retry in retry_lib.sh, so it stays in lockstep with train_slurm.py.
 #
 # Positional args (main path):
 #   1  PYTHON      interpreter invocation, e.g. "/workspace/isaaclab/isaaclab.sh -p"
 #   2  SIMULATOR   isaacgym|isaaclab|newton (selects install path + IsaacLab warmup)
 #   3  EXP_NAME    experiment name; checkpoints live at results/<EXP_NAME>/last.ckpt
 #   4  EXP_FOLDER  code snapshot on AMLFS; cwd for training, holds results/
-#   5  MAX_RUNS    crash-restart cap (loop iterations)
+#   5  MAX_RUNS    hard cap on total loop iterations (RETRY_TOTAL_ATTEMPTS)
 #   6+ the train_agent.py argument tail, forwarded verbatim as "$@"
 #
 # Each positional is shlex-quoted individually by train_lepton.py, so the
 # container shell splits the command back into exactly these arguments and the
 # train_agent.py tail reaches "$@" without a value being word-split on a space.
 #
-# Env knobs (defaults in parentheses):
-#   STALL_TIMEOUT_MIN (30)  kill training if the durable log stops growing for this
-#                           many minutes; 0 disables. Passed by train_lepton.py.
-#   NO_PROGRESS_LIMIT (3)   give up after this many consecutive runs that do not
-#                           advance last.ckpt (deterministic failure).
+# Env knobs (defaults in parentheses; passed by train_lepton.py):
+#   STALL_TIMEOUT_MIN (30)   kill training if the durable log stops growing for
+#                            this many minutes; 0 disables.
+#   RETRY_MAX_ATTEMPTS (3)   give up after this many consecutive runs that do not
+#                            advance last.ckpt (deterministic failure).
+#   RETRY_REPEAT_LIMIT (3)   give up sooner if the SAME Python exception repeats
+#                            this many runs in a row (the fast-fail-on-python path).
 # Test-only knobs (see protomotions/tests/test_lepton_train_loop.py):
-#   LEPTON_LOOP_SOURCE_ONLY  when set, `source` this file to get the functions
-#                            below (kill_tree/run_with_stall_watchdog/...) WITHOUT
-#                            running any of the main install+train path.
+#   LEPTON_LOOP_SOURCE_ONLY  when set, `source` this file to get the shared
+#                            functions (kill_tree/run_with_stall_watchdog/
+#                            run_with_retry/...) WITHOUT running the main path.
 #   LEPTON_LOOP_SKIP_SETUP   skip the per-container install + IsaacLab warmup, so
 #                            the loop itself can be exercised with a fake python.
 #   STALL_POLL_SEC (60)      watchdog poll interval; sub-second in tests.
@@ -46,123 +52,16 @@
 set -uo pipefail
 
 # ===========================================================================
-# Functions. Defined first so LEPTON_LOOP_SOURCE_ONLY tests can source them.
+# Shared retry/watchdog primitives.
 # ===========================================================================
-
-ckpt_stamp() {
-    # last.ckpt mtime in epoch-seconds, or "none" while it does not exist yet.
-    [ -f "$CKPT" ] && stat -c %Y "$CKPT" 2>/dev/null || echo "none"
-}
-
-kill_tree() {
-    # Kill a process AND all its descendants, children first. The training python
-    # and its Fabric worker ranks live several levels below the process we
-    # background (for IsaacLab, the isaaclab.sh wrapper). Killing only the parent
-    # reparents the grandchildren to init -- they keep the GPUs and MASTER_PORT,
-    # and the immediate resume then collides. Recurse depth-first so a parent is
-    # never killed before its children.
-    local root=$1 child
-    for child in $(pgrep -P "$root" 2>/dev/null); do
-        kill_tree "$child"
-    done
-    kill -9 "$root" 2>/dev/null
-}
-
-run_with_stall_watchdog() {
-    # Run "$@", and if $LOGFILE stops growing for STALL_QUIET_LIMIT consecutive
-    # polls of STALL_POLL_SEC seconds, kill the whole process tree so the loop
-    # restarts instead of hanging. A wedged rank -- one stuck at setup while the
-    # others block at the distributed barrier -- emits no output and no exit code,
-    # but a blocked collective silences the HEALTHY ranks too, so "the durable log
-    # stopped growing" is a reliable whole-job-wedge signal. Defaults poll every
-    # 60s with the limit in minutes (STALL_TIMEOUT_MIN); a limit <= 0 disables it.
-    local poll="${STALL_POLL_SEC:-60}"
-    local quiet_limit="${STALL_QUIET_LIMIT:-${STALL_TIMEOUT_MIN:-30}}"
-    "$@" &
-    local pid=$!
-    if [ "${quiet_limit}" -le 0 ]; then
-        wait "$pid"
-        return $?
-    fi
-    (
-        last=-1
-        quiet=0
-        while kill -0 "$pid" 2>/dev/null; do
-            sleep "$poll"
-            size=$(stat -c %s "$LOGFILE" 2>/dev/null || echo 0)
-            if [ "$size" != "$last" ]; then
-                last=$size
-                quiet=0
-            else
-                quiet=$((quiet + 1))
-            fi
-            if [ "$quiet" -ge "${quiet_limit}" ]; then
-                echo "[watchdog] ${LOGFILE} has not grown for ${quiet_limit} poll(s)" \
-                    "of ${poll}s -- training looks wedged (a rank stuck at" \
-                    "setup/barrier?). Killing pid ${pid} and its descendants so the" \
-                    "job restarts instead of burning the walltime." >&2
-                kill_tree "$pid"
-                break
-            fi
-        done
-    ) &
-    local watchdog=$!
-    wait "$pid"
-    local status=$?
-    kill "$watchdog" 2>/dev/null
-    wait "$watchdog" 2>/dev/null
-    return "$status"
-}
-
-run_training_loop() {
-    # Restart train_agent.py after a crash; give up only when a run makes no
-    # forward progress -- last.ckpt's mtime does not advance -- for
-    # NO_PROGRESS_LIMIT runs in a row. Keying on progress (not merely "does a
-    # checkpoint exist?") handles both failure modes the simpler test gets wrong: a
-    # deterministic crash *after* the first checkpoint (otherwise it would loop the
-    # identical failure for the whole budget, resuming from the same epoch and
-    # re-crashing without ever saving), and a *transient* crash *before* the first
-    # checkpoint (otherwise a single early flake fails the job). A genuinely
-    # deterministic startup error still fails fast, after NO_PROGRESS_LIMIT quick
-    # attempts. "$@" is the train_agent.py argument tail.
-    local no_progress=0 r before after status
-    for r in $(seq 1 "$MAX_RUNS"); do
-        echo "[lepton-loop] ===== run ${r}/${MAX_RUNS} ====="
-        before=$(ckpt_stamp)
-        # Identical command every iteration: train_agent.py auto-resumes when
-        # results/<exp>/last.ckpt exists. No --use-slurm (no walltime autoresume).
-        # shellcheck disable=SC2086  # $PYTHON is "cmd -flag": word-split intended.
-        # The train tail is "$@" -- quoted, so each argument is preserved.
-        run_with_stall_watchdog $PYTHON -u protomotions/train_agent.py "$@"
-        status=$?
-
-        if [ "$status" -eq 0 ]; then
-            echo "[lepton-loop] run ${r} exited 0 -- training complete."
-            return 0
-        fi
-
-        after=$(ckpt_stamp)
-        if [ "$after" != "$before" ]; then
-            no_progress=0
-            echo "[lepton-loop] run ${r} crashed (exit ${status}); checkpoint advanced -- resuming."
-        else
-            no_progress=$((no_progress + 1))
-            echo "[lepton-loop] run ${r} crashed (exit ${status}); no checkpoint progress" \
-                "(${no_progress}/${NO_PROGRESS_LIMIT} in a row)." >&2
-            if [ "$no_progress" -ge "$NO_PROGRESS_LIMIT" ]; then
-                echo "[lepton-loop] ${NO_PROGRESS_LIMIT} runs in a row made no progress; this is" >&2
-                echo "[lepton-loop] almost certainly a deterministic failure. Giving up -- see the" >&2
-                echo "[lepton-loop] traceback(s) above." >&2
-                touch "$GAVE_UP"
-                return "$status"
-            fi
-            echo "[lepton-loop] retrying in case the crash was transient."
-        fi
-    done
-
-    echo "[lepton-loop] exhausted ${MAX_RUNS} run(s) without completing." >&2
-    return 1
-}
+# kill_tree, run_with_stall_watchdog, run_with_retry and ckpt_stamp live in
+# retry_lib.sh so this loop and the SLURM launcher share one implementation.
+# Source by absolute path from THIS script's own directory so it resolves no
+# matter the cwd, and so a LEPTON_LOOP_SOURCE_ONLY test that sources this file
+# gets those functions transitively.
+_LOOP_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+# shellcheck source=retry_lib.sh
+source "${_LOOP_DIR}/retry_lib.sh"
 
 # When a test sources this file just for the functions above, stop here -- do not
 # parse positionals, install, or train.
@@ -196,7 +95,9 @@ exec > >(tee -a "$LOGFILE") 2>&1
 # exiting 0 here would let the job's final state read as "completed" even though
 # training never succeeded, which is dangerous for any automation watching the job
 # status. train_lepton.py clears this sentinel when you deliberately resume with
-# --exp-folder; remove it by hand otherwise to allow retries.
+# --exp-folder; remove it by hand otherwise to allow retries. Checked here, before
+# the expensive install+warmup, so a known-dead relaunch exits fast (run_with_retry
+# re-checks it too, but only after setup).
 GAVE_UP="${EXP_FOLDER}/.lepton_loop_gave_up"
 if [ -f "$GAVE_UP" ]; then
     echo "[lepton-loop] a previous attempt gave up (deterministic failure; see the" \
@@ -206,15 +107,25 @@ if [ -f "$GAVE_UP" ]; then
     exit 1
 fi
 
+# Map the Lepton knobs onto the shared run_with_retry environment. Continue on
+# progress: with no walltime, resume in place until training completes, capped at
+# MAX_RUNS total iterations. RETRY_BACKOFF_SEC=0 preserves the original
+# immediate-resume timing.
 CKPT="results/${EXP_NAME}/last.ckpt"
-NO_PROGRESS_LIMIT="${NO_PROGRESS_LIMIT:-3}"
-STALL_TIMEOUT_MIN="${STALL_TIMEOUT_MIN:-30}"
+export CKPT LOGFILE GAVE_UP
+export RETRY_MAX_ATTEMPTS="${RETRY_MAX_ATTEMPTS:-3}"
+export RETRY_TOTAL_ATTEMPTS="${MAX_RUNS}"
+export RETRY_REPEAT_LIMIT="${RETRY_REPEAT_LIMIT:-3}"
+export RETRY_ON_PROGRESS="continue"
+export RETRY_BACKOFF_SEC="${RETRY_BACKOFF_SEC:-0}"
+export STALL_TIMEOUT_MIN="${STALL_TIMEOUT_MIN:-30}"
 
 echo "[lepton-loop] host=$(hostname) exp=${EXP_NAME}"
 echo "[lepton-loop] folder=${EXP_FOLDER}"
 echo "[lepton-loop] durable log: ${LOGFILE}"
 echo "[lepton-loop] python=${PYTHON} simulator=${SIMULATOR} max_runs=${MAX_RUNS}" \
-    "no_progress_limit=${NO_PROGRESS_LIMIT} stall_timeout_min=${STALL_TIMEOUT_MIN}"
+    "no_progress_limit=${RETRY_MAX_ATTEMPTS} repeat_limit=${RETRY_REPEAT_LIMIT}" \
+    "stall_timeout_min=${STALL_TIMEOUT_MIN}"
 
 # --- one-time per-container setup -------------------------------------------
 # Import the staged snapshot as an editable install; runtime deps already live in
@@ -279,5 +190,9 @@ echo "[lepton-loop] rendezvous: NNODES=${NNODES} NODE_RANK=${NODE_RANK}" \
     "MASTER_ADDR=${MASTER_ADDR} MASTER_PORT=${MASTER_PORT}"
 
 # --- run --------------------------------------------------------------------
-run_training_loop "$@"
+# run_with_retry resumes after a crash; run_with_stall_watchdog (inside it) kills a
+# hung train_agent.py so the loop sees an exit. $PYTHON is "cmd -flag" -- the
+# word-split is intended; the train tail is quoted "$@" so each argument survives.
+# shellcheck disable=SC2086
+run_with_retry run_with_stall_watchdog $PYTHON -u protomotions/train_agent.py "$@"
 exit $?
diff --git a/scripts/cluster/retry_lib.sh b/scripts/cluster/retry_lib.sh
new file mode 100644
index 000000000..b6a851639
--- /dev/null
+++ b/scripts/cluster/retry_lib.sh
@@ -0,0 +1,306 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Shared cluster retry/watchdog primitives, SOURCED by both launchers so their
+# retry logic cannot drift:
+#   - train_slurm.py's generated sbatch sources this at the batch layer (to wrap
+#     srun in run_with_retry) and again inside srun (to wrap train_agent.py in
+#     run_with_stall_watchdog).
+#   - lepton_train_loop.sh sources it once (in-job) for both.
+#
+# This file is only ever sourced, never executed. It defines four primitives:
+#
+#   kill_tree <pid>
+#       Kill a process AND all descendants, children first. Only reaches processes
+#       still reachable through parent/child links.
+#
+#   reap_process_group <pgid>
+#       Kill whatever is left in a process group, TERM then KILL. Complements
+#       kill_tree, which cannot follow a process that has already been reparented
+#       to init -- a reparented process keeps its process group, so this catches
+#       the ranks a crashed attempt leaves behind.
+#
+#   run_with_stall_watchdog <cmd...>       -- THE one watchdog.
+#       Run <cmd...>; if $LOGFILE stops growing for a while, kill its whole tree
+#       so the caller sees an exit instead of a hang. This is the ONLY liveness
+#       check: a hung scene build is caught here (it is armed from process start,
+#       before training), and for a known-flaky scene build you lower its timeout
+#       (STALL_TIMEOUT_MIN) rather than adding a second, startup-only watchdog.
+#
+#   run_with_retry <cmd...>
+#       Run <cmd...> repeatedly. <cmd...> is expected to itself run training under
+#       run_with_stall_watchdog (Lepton passes it inline; SLURM's srun wraps it),
+#       so a hang is already killed by that watchdog and this loop only ever sees
+#       the resulting exit code. Per attempt:
+#         exit 0                             -> done.
+#         $CKPT's mtime advanced this attempt (training saved a checkpoint):
+#             RETRY_ON_PROGRESS=break        -> stop; the SLURM array's next task
+#                                               resumes from the checkpoint.
+#             RETRY_ON_PROGRESS=continue     -> reset the no-progress streak and
+#                                               loop; Lepton resumes in place.
+#         no progress, SAME Python exception class RETRY_REPEAT_LIMIT times in a
+#         row                                -> deterministic bug: give up, surface
+#                                               it (this is the Python fast-fail).
+#         no progress, native abort / new exception
+#                                            -> flaky: retry, bounded.
+#
+# Env knobs for run_with_retry (all optional):
+#   RETRY_MAX_ATTEMPTS   (1)   consecutive no-progress attempts before giving up.
+#   RETRY_TOTAL_ATTEMPTS (=MAX_ATTEMPTS)  hard cap on total attempts; only matters
+#                              in continue mode, where progress resets the
+#                              no-progress streak and could otherwise loop forever.
+#   RETRY_REPEAT_LIMIT   (3, 0=off)  same-exception-class attempts before fast-fail.
+#   RETRY_BACKOFF_SEC    (15)  sleep between no-progress retries.
+#   RETRY_ON_PROGRESS    (continue|break)  see above.
+#   CKPT                       last.ckpt path; its mtime is the progress signal.
+#   LOGFILE                    log to slice for the exception-class check.
+#   GAVE_UP                    optional sentinel path. When set, a give-up touches
+#                              it and a pre-existing one short-circuits to a nonzero
+#                              exit (so a relaunch is marked failed, not falsely
+#                              successful). Unset -> no sentinel (the SLURM array
+#                              re-runs and fast-fails again per task instead).
+#
+# Env knobs for run_with_stall_watchdog: STALL_TIMEOUT_MIN (30, 0=off) is the
+# user-facing one (no-growth minutes); STALL_POLL_SEC (60) and STALL_QUIET_LIMIT
+# (=STALL_TIMEOUT_MIN) let a test drive it in fractions of a second.
+# STALL_IGNORE_REGEX overrides which lines are treated as dead-rank noise rather
+# than progress, and REAP_GRACE_SEC (5) is how long survivors get between the
+# reaper's SIGTERM and its SIGKILL.
+#
+# Test hook: RETRY_LIB_SOURCE_ONLY is accepted for symmetry with
+# lepton_train_loop.sh, but since this file has no main path it is a no-op here --
+# a test just sources the file and calls the functions.
+
+ckpt_stamp() {
+    # $CKPT's mtime in epoch-seconds, or "none" while it does not exist yet.
+    # base_agent writes last.ckpt at loop entry (epoch 0), so this advances the
+    # moment training is reached and a long-first-epoch run still leaves a resume
+    # point before its first periodic save.
+    [ -n "${CKPT:-}" ] && [ -f "$CKPT" ] && stat -c %Y "$CKPT" 2>/dev/null || echo "none"
+}
+
+kill_tree() {
+    # Kill a process AND all its descendants, children first. The training python
+    # and its Fabric worker ranks live several levels below the process we
+    # background; killing only the parent reparents the grandchildren to init --
+    # they keep the GPUs and MASTER_PORT, and the immediate resume then collides.
+    # Recurse depth-first so a parent is never killed before its children.
+    local root=$1 child
+    for child in $(pgrep -P "$root" 2>/dev/null); do
+        kill_tree "$child"
+    done
+    kill -9 "$root" 2>/dev/null
+}
+
+reap_process_group() {
+    # Kill anything still alive in process group $1 (== the attempt's pid, because
+    # run_with_stall_watchdog starts it under "set -m"). Complements kill_tree:
+    # kill_tree walks the parent/child links and so cannot reach a process that
+    # has already been reparented to init, whereas a reparented process keeps its
+    # process group. Safe to call when the group is already empty.
+    #
+    # SIGTERM first so a rank can release its GPU context, then SIGKILL whatever
+    # ignored it. Without the grace period a killed rank can leave the device in a
+    # state the next attempt trips over.
+    local pgid=$1
+    kill -- -"$pgid" 2>/dev/null || return 0
+    local waited=0
+    while kill -0 -- -"$pgid" 2>/dev/null && [ "$waited" -lt "${REAP_GRACE_SEC:-5}" ]; do
+        sleep 1
+        waited=$((waited + 1))
+    done
+    kill -9 -- -"$pgid" 2>/dev/null
+    return 0
+}
+
+run_with_stall_watchdog() {
+    # Run "$@", and if $LOGFILE stops growing for STALL_QUIET_LIMIT consecutive
+    # polls of STALL_POLL_SEC seconds, kill the whole process tree so the caller
+    # restarts instead of hanging. A wedged rank -- one stuck at setup while the
+    # others block at a distributed barrier -- emits no output and no exit code,
+    # but a blocked collective silences the HEALTHY ranks too, so "the durable log
+    # stopped growing" is a reliable whole-job-wedge signal. Defaults poll every
+    # 60s with the limit in minutes (STALL_TIMEOUT_MIN); a limit <= 0 disables it.
+    #
+    # That "silences the healthy ranks too" assumption holds when a rank is WEDGED
+    # (alive but stuck). It does NOT hold when a rank DIES: the survivors then stop
+    # blocking and start emitting NCCL heartbeat / TCPStore "Broken pipe" errors
+    # several lines a second, forever. Measured on a real 4-rank job where ranks 0
+    # and 1 vanished silently and the log kept growing at that rate -- so the raw
+    # file size never stopped changing and the watchdog could never fire. The one
+    # failure mode most needing a kill was the one it slept through.
+    #
+    # So measure PROGRESS, not bytes: count only lines that are not dead-rank
+    # noise. A half-dead job then looks correctly quiet, while a healthy job (whose
+    # progress lines are not filtered) is unaffected. Checkpoint mtime is not usable
+    # as the liveness signal here -- last.ckpt does not exist during the scene build
+    # this watchdog exists to cover, and save_last_checkpoint_every defaults to 10
+    # epochs, far coarser than the timeout.
+    local poll="${STALL_POLL_SEC:-60}"
+    local quiet_limit="${STALL_QUIET_LIMIT:-${STALL_TIMEOUT_MIN:-30}}"
+    # Overridable so a site with different noise can extend it, and so tests can
+    # drive it. Anchored where possible to avoid filtering real content.
+    local noise="${STALL_IGNORE_REGEX:-ProcessGroupNCCL|TCPStore|Broken pipe|^frame #|^Exception raised from|should dump}"
+    # Run the attempt in its OWN PROCESS GROUP ("set -m" makes a background job a
+    # group leader, so PGID == pid). When one rank aborts, the launcher does not
+    # always reap its siblings; those survivors reparent to init, and kill_tree's
+    # pgrep -P walk cannot follow them once the direct child is gone. A process
+    # group can: orphans keep their PGID.
+    #
+    # Measured leak this closes: on a Lepton job, attempts 1, 2 and 4 each left
+    # ranks behind, so by attempt 5 several stale IsaacLab processes were still
+    # holding GPU contexts and PhysX buffers -- and spamming NCCL errors once a
+    # second, which is what drowned the healthy run's output. kill_tree's own
+    # comment already predicted this ("they keep the GPUs and MASTER_PORT, and the
+    # immediate resume then collides"); the guard was simply never wired into the
+    # crash path, only into the watchdog's.
+    set -m
+    "$@" &
+    local pid=$!
+    set +m
+    if [ "${quiet_limit}" -le 0 ]; then
+        wait "$pid"
+        local status_no_wd=$?
+        reap_process_group "$pid"
+        return "$status_no_wd"
+    fi
+    (
+        last=-1
+        quiet=0
+        while kill -0 "$pid" 2>/dev/null; do
+            sleep "$poll"
+            # Count of non-noise lines, not file size: dead-rank NCCL spam must not
+            # read as progress. grep -a so a binary-looking log still counts.
+            size=$(grep -acvE "$noise" "$LOGFILE" 2>/dev/null || echo 0)
+            if [ "$size" != "$last" ]; then
+                last=$size
+                quiet=0
+            else
+                quiet=$((quiet + 1))
+            fi
+            if [ "$quiet" -ge "${quiet_limit}" ]; then
+                echo "[watchdog] ${LOGFILE} has produced no non-noise output for" \
+                    "${quiet_limit} poll(s)" \
+                    "of ${poll}s -- training looks wedged (a rank stuck at" \
+                    "setup/barrier?). Killing pid ${pid} and its descendants so the" \
+                    "caller restarts instead of burning the walltime." >&2
+                kill_tree "$pid"
+                break
+            fi
+        done
+    ) &
+    local watchdog=$!
+    wait "$pid"
+    local status=$?
+    kill "$watchdog" 2>/dev/null
+    wait "$watchdog" 2>/dev/null
+    reap_process_group "$pid"
+    return "$status"
+}
+
+run_with_retry() {
+    local nomax="${RETRY_MAX_ATTEMPTS:-1}"
+    local total="${RETRY_TOTAL_ATTEMPTS:-$nomax}"
+    local repeat_limit="${RETRY_REPEAT_LIMIT:-3}"
+    local backoff="${RETRY_BACKOFF_SEC:-15}"
+    local on_progress="${RETRY_ON_PROGRESS:-continue}"
+    # Always run at least once: a 0/negative value must not skip the command and
+    # report a false success. Guard the total the same way, but do NOT raise it to
+    # nomax -- an explicit small total (e.g. Lepton's --max-runs below
+    # --no-progress-limit) is a legitimate hard cap to honour, whichever bound trips
+    # first.
+    [ "$nomax" -lt 1 ] && nomax=1
+    [ "$total" -lt 1 ] && total=1
+
+    # A give-up sentinel from a PRIOR attempt (deterministic failure, no progress).
+    # Exit NONZERO so this relaunch is marked failed rather than falsely
+    # successful: exiting 0 here would let a job's final state read as "completed"
+    # even though training never succeeded, which is dangerous for automation
+    # watching the job status.
+    if [ -n "${GAVE_UP:-}" ] && [ -f "$GAVE_UP" ]; then
+        echo "[retry] a previous attempt gave up (deterministic failure; see the log" \
+            "above). Exiting NONZERO so this job is marked failed, not falsely" \
+            "successful. Remove ${GAVE_UP} to allow retries." >&2
+        return 1
+    fi
+
+    local attempt=0 no_progress=0 status=0 prev_cls="" repeat=0
+    local before after _mark _exc _cls
+    while [ "$attempt" -lt "$total" ]; do
+        attempt=$((attempt + 1))
+        before=$(ckpt_stamp)
+        # Read only THIS attempt's slice of the shared log for the exception check:
+        # everything from line _mark+1 on.
+        if [ -f "${LOGFILE:-}" ]; then _mark=$(wc -l < "$LOGFILE"); else _mark=0; fi
+
+        "$@"
+        status=$?
+
+        if [ "$status" -eq 0 ]; then
+            echo "[retry] attempt ${attempt} exited 0 -- done."
+            return 0
+        fi
+
+        after=$(ckpt_stamp)
+        if [ "$after" != "$before" ]; then
+            # Progress: training saved a checkpoint during this attempt, so it got
+            # past setup. A failure now is a mid-run crash / walltime kill, resumed
+            # from the checkpoint -- never a flaky scene build to bang on in place.
+            if [ "$on_progress" = "break" ]; then
+                echo "[retry] attempt ${attempt} exited ${status} after a checkpoint was" \
+                    "written -- training was reached; not retrying in place (the array" \
+                    "element resumes from the checkpoint)." >&2
+                return "$status"
+            fi
+            no_progress=0
+            prev_cls=""
+            repeat=0
+            echo "[retry] attempt ${attempt} crashed (exit ${status}); checkpoint" \
+                "advanced -- resuming."
+            continue
+        fi
+
+        # No progress. Classify: a native abort/hang prints no Python traceback, so
+        # retry it freely (bounded). A DETERMINISTIC bug prints the same exception
+        # every attempt -- stop and surface it rather than hide it behind retries.
+        # Match message-LESS exceptions too (a bare `assert` prints just
+        # "AssertionError", no colon -- a common deterministic startup bug). Compare
+        # only the exception CLASS (strip the message): a message that embeds a
+        # path, CUDA device id or rank varies between attempts, but the bug is one.
+        no_progress=$((no_progress + 1))
+        # ${LOGFILE:-} (not bare $LOGFILE) so this cannot abort under `set -u` if a
+        # caller ever leaves LOGFILE unset -- it just yields an empty slice, routing
+        # to the native-abort (retry) path. Mirrors the guard on the _mark read above.
+        _exc=$(tail -n +$((_mark + 1)) "${LOGFILE:-}" 2>/dev/null | grep -oE '^[A-Za-z_][A-Za-z0-9_.]*(Error|Exception)(: .*)?$' | tail -1)
+        _cls="${_exc%%:*}"
+        if [ -n "$_exc" ] && [ "$_cls" = "$prev_cls" ]; then
+            repeat=$((repeat + 1))
+        elif [ -n "$_exc" ]; then
+            repeat=1
+        fi
+        prev_cls="$_cls"
+        if [ -n "$_exc" ] && [ "$repeat_limit" -gt 0 ] && [ "$repeat" -ge "$repeat_limit" ]; then
+            echo "[retry] same exception ${repeat} attempts in a row -- this is" \
+                "deterministic, not flaky:" >&2
+            echo "[retry]   ${_exc}" >&2
+            [ -n "${GAVE_UP:-}" ] && touch "$GAVE_UP"
+            return "$status"
+        fi
+        if [ "$no_progress" -ge "$nomax" ]; then
+            echo "[retry] ${no_progress} attempt(s) in a row made no progress -- almost" \
+                "certainly a deterministic failure. Giving up -- see the traceback(s)" \
+                "above." >&2
+            [ -n "${GAVE_UP:-}" ] && touch "$GAVE_UP"
+            return "$status"
+        fi
+        echo "[retry] attempt ${attempt} failed (exit ${status}) with no progress;" \
+            "retrying in ${backoff}s in case it was transient." >&2
+        sleep "$backoff"
+    done
+
+    # Ran out of the total budget while still making occasional progress but never
+    # completing. Not necessarily deterministic, so do NOT write the sentinel -- a
+    # fresh relaunch should be free to continue from the last checkpoint.
+    echo "[retry] exhausted ${total} attempt(s) without completing." >&2
+    return "$status"
+}

From 92202c52fe2347703bf08e0a28dabc46392f7fc9 Mon Sep 17 00:00:00 2001
From: protomotions <protomotions.nvidia@gmail.com>
Date: Fri, 16 Oct 2026 12:27:42 -0700
Subject: [PATCH 2/2] docs: clarify viewer key bindings

Signed-off-by: protomotions <protomotions.nvidia@gmail.com>
---
 docs/source/api_reference/scripts.rst | 19 +++++++------------
 1 file changed, 7 insertions(+), 12 deletions(-)

diff --git a/docs/source/api_reference/scripts.rst b/docs/source/api_reference/scripts.rst
index 0c02fa49d..95e352f20 100644
--- a/docs/source/api_reference/scripts.rst
+++ b/docs/source/api_reference/scripts.rst
@@ -70,22 +70,17 @@ For kinematic motion playback without physics training state:
 
 **Keyboard Controls:**
 
+Click inside the simulation viewer to give it keyboard focus, then use the
+task's key bindings.
+
 * **W/A/S/D** - Move keyboard-controlled task targets when enabled
 * **R** - Reset environments
 * **Q** - Quit
 
-For MuJoCo, click the simulation viewer itself to give it keyboard focus;
-there is no separate controls window. Keys produce discrete presses (held-key
-state is not available from the passive viewer). Application callbacks run on
-the simulation thread before physics, not on the viewer thread.
-
-MuJoCo also assigns visualization shortcuts to task keys such as W, R and L.
-Task mode restores the startup visualization flags and geometry groups at each
-render synchronization. Press **.** to switch to native viewer shortcuts for
-debugging; task commands are disabled in that mode. Press **.** again to resume
-task controls and restore the startup visualization settings. Mouse orbit and
-zoom remain available in both modes. Native shortcuts execute before the Python
-callback, so a transient visual change may appear before the next synchronization.
+**MuJoCo only:** press **.** to toggle between ProtoMotions key bindings
+(default) and MuJoCo's native viewer key bindings. Only the selected set is
+active. Switching back to ProtoMotions key bindings restores the initial
+visualization settings. Mouse orbit and zoom work in either mode.
 
 train_slurm.py
 --------------