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refactor(rewards): update imports and delete old reward model locations

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Khalil Meftah
2026-03-12 15:23:42 +01:00
parent 0b992cfbd6
commit 445ffae5ce
14 changed files with 31 additions and 3209 deletions
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docs/source/sarm.mdx
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@@ -46,7 +46,7 @@ This ensures identical task states map to consistent progress values, even acros
## Inputs and Targets (What the new code expects)
SARM is trained through its processor (`src/lerobot/policies/sarm/processor_sarm.py`), which:
SARM is trained through its processor (`src/lerobot/rewards/sarm/processor_sarm.py`), which:
- **Encodes** images and task text with CLIP (ViT-B/32) into `video_features` and `text_features`
- **Pads/truncates** robot state into `state_features` (up to `max_state_dim`)
@@ -347,7 +347,7 @@ Use `compute_rabc_weights.py` with `--visualize-only` to visualize model predict
<hfoption id="single_stage">
```bash
python src/lerobot/policies/sarm/compute_rabc_weights.py \
python -m lerobot.rewards.sarm.compute_rabc_weights \
--dataset-repo-id your-username/your-dataset \
--reward-model-path your-username/sarm-model \
--visualize-only \
@@ -360,7 +360,7 @@ python src/lerobot/policies/sarm/compute_rabc_weights.py \
<hfoption id="dense_only">
```bash
python src/lerobot/policies/sarm/compute_rabc_weights.py \
python -m lerobot.rewards.sarm.compute_rabc_weights \
--dataset-repo-id your-username/your-dataset \
--reward-model-path your-username/sarm-model \
--visualize-only \
@@ -373,7 +373,7 @@ python src/lerobot/policies/sarm/compute_rabc_weights.py \
<hfoption id="dual">
```bash
python src/lerobot/policies/sarm/compute_rabc_weights.py \
python -m lerobot.rewards.sarm.compute_rabc_weights \
--dataset-repo-id your-username/your-dataset \
--reward-model-path your-username/sarm-model \
--visualize-only \
@@ -429,7 +429,7 @@ The weighting follows **Equations 8-9** from the paper:
First, run the SARM model on all frames in your dataset to compute progress values:
```bash
python src/lerobot/policies/sarm/compute_rabc_weights.py \
python -m lerobot.rewards.sarm.compute_rabc_weights \
--dataset-repo-id your-username/your-dataset \
--reward-model-path your-username/sarm-model \
--head-mode sparse \