Add model card

README.md

@@ -0,0 +1,72 @@
+---
+tags:
+  - robotics
+  - vla
+  - rl-token
+---
+
+# pi05-so101-armnetbench-tool-insert-rlt-v50
+
+RL Token (RLT) encoder-decoder trained on the SO101 tool insertion task, on top of the **step_24999** checkpoint from [lorenzouttini/pi05-so101-armnetbench-tool-insert-isambard-v50](https://huggingface.co/lorenzouttini/pi05-so101-armnetbench-tool-insert-isambard-v50).
+
+## What is this?
+
+This model is a lightweight transformer encoder-decoder which takes inputs from a frozen Pi-05 VLA backbone. The encoder compresses the VLA final-layer prefix embeddings into a single RL token via a learned query. The decoder autoregressively reconstructs the original embeddings from only this token, forcing it to act as an information bottleneck. See [Xu et al. (2026), Precise Manipulation with Efficient Online RL](https://www.pi.website/research/rlt) for the method.
+
+## Training
+
+- **Config:** `pi05_rlt_armnetbench_tool_insert`
+- **VLA backbone:** `lorenzouttini/pi05-so101-armnetbench-tool-insert-isambard-v50` step_24999 (frozen, `rl_vla_loss_weight=0.0`)
+- **Encoder-decoder:** 2-layer transformer, 8 heads, 8192 MLP dim, 2048 embedding dim
+- **Dataset:** `villekuosmanen/armnetbench_tool_insert`
+- **Batch size:** 32
+- **LR:** 2.5e-5 cosine (1k warmup, 20k decay)
+- **Steps:** 20,000
+- **Runtime:** ~4h14m on 4x GH200 (Isambard)
+
+No validation split was used — the dataset is too small for a held-out eval split.
+
+## Loss progression (train)
+
+| Step | Train Loss |
+|------|-----------|
+| 0 | 10754.3 |
+| 1,000 | 873.7 |
+| 5,000 | 552.0 |
+| 10,000 | 430.0 |
+| 15,000 | 377.6 |
+| 19,900 | 356.4 |
+
+## Checkpoints
+
+| Step | Recommended | Params SHA256 |
+|------|-------------|---------------|
+| 19999 | ✓ | `d9ddbbbefc07b3700f7df5dd161c14de3291bfcf805f71e39ababb902e1501b2` |
+
+### Verifying checkpoint hashes
+
+```bash
+cd checkpoints/19999 && find params -type f | sort | xargs sha256sum | sha256sum
+```
+
+## Repo layout
+
+```
+assets/                         # Norm stats, valid indices
+checkpoints/19999/params/       # Step 19999 model weights (recommended)
+```
+
+## W&B
+
+Training curves: https://wandb.ai/pravsels/pi05_rlt_armnetbench_tool_insert/runs/jbdqrmu0
+
+## Usage
+
+```python
+import openpi.models.model as _model
+import openpi.training.config as _config
+
+config = _config.get_config("pi05_rlt_armnetbench_tool_insert")
+params = _model.restore_params("checkpoints/19999/params", restore_type=np.ndarray)
+model = config.model.load(params)
+```