fixing training and exal examples

docs/source/policy_vla_jepa_README.md

@@ -28,14 +28,6 @@ Only Qwen + the action head are used. The world model is not needed at inference
 
 ### Action head details
 
-The action head is a **Diffusion Transformer (DiT-B)** with flow matching:
-
-- **Inner dim**: 768 (12 heads × 64 head dim, DiT-B preset)
-- **Output dim**: `action_hidden_size` (default 1024), projected down to `action_dim`
-- **Cross/self alternation**: even-indexed DiT blocks attend to Qwen context tokens (cross-attention); odd-indexed blocks are self-attention
-- **Noise schedule**: Beta distribution with parameters `action_noise_beta_alpha` / `action_noise_beta_beta`
-- **Inference**: Euler integration over `num_inference_timesteps` steps
-
 Available presets via `action_model_type`:
 
 | Preset  | Hidden dim | Heads | Head dim |
@@ -68,14 +60,6 @@ Three checkpoints are available, converted from [ginwind/VLA-JEPA](https://huggi
 
 All checkpoints use `Qwen/Qwen3-VL-2B-Instruct` as the language backbone.
 
-### Loading a pretrained checkpoint
-
-```python
-from lerobot.policies.vla_jepa.modeling_vla_jepa import VLAJEPAPolicy
-
-policy = VLAJEPAPolicy.from_pretrained("lerobot/VLA-JEPA-LIBERO")
-```
-
 ---
 
 ## Configuration
@@ -96,49 +80,48 @@ Key parameters in `VLAJEPAConfig`:
 
 ## Training
 
+Number of training steps may vary based on dataset size and compute budget. The original paper pretrained for 50k on ssv2 + droid jointly, then additional 30k steps for LIBERO, but fewer steps may still yield good performance when fine-tuning from the provided pretrained checkpoints.
+
 ### Full training from scratch
 
 ```bash
 lerobot-train \
-  dataset.repo_id=your_org/your_dataset \
-  policy.chunk_size=16 \
-  policy.n_action_steps=16
+  policy.type=vla_jepa \
+  policy.repo_id=your_org/your_repo \
+  dataset.repo_id=your_org/your_dataset
 ```
 
 ### Fine-tuning from a pretrained checkpoint
 
 ```bash
 lerobot-train \
-  policy.path=lerobot/VLA-JEPA-Pretrain \
-  dataset.repo_id=your_org/your_dataset
+  --policy.path=lerobot/VLA-JEPA-Pretrain \
+  --policy.repo_id=your_org/your_repo \
+  --dataset.repo_id=your_org/your_dataset
 ```
 
 If you want to go further and freeze the Qwen backbone and only train the action head, set `policy.freeze_qwen=True`:
 
 ```bash
 lerobot-train \
-  policy.path=lerobot/VLA-JEPA-Pretrain \
-  dataset.repo_id=your_org/your_dataset \
-  policy.freeze_qwen=true
+  --policy.path=lerobot/VLA-JEPA-Pretrain \
+  --policy.repo_id=your_org/your_repo \
+  --policy.freeze_qwen=true \
+  --dataset.repo_id=your_org/your_dataset
 ```
 
 ### Reproducing the LIBERO results
 
 **Training on LIBERO:**
-
-TODO(Maxime):
-
-- [ ] double check the training command
-- [ ] double check which LIBERO dataset (libero_10 or full libero) was used for training the checkpoint
-- [ ] add the evaluation command for the pretrained checkpoint + check that the results match the original paper
+starts the training from the Pretrain checkpoint, trains for 30k steps on the LIBERO dataset.
+Original paper mentions training across 8 GPUs with a batch size of 32, meaning global batch size of 256.
 
 ```bash
 lerobot-train \
-  policy.path=lerobot/VLA-JEPA-Pretrain \
-  dataset.repo_id=lerobot/libero_10 \
-  training.num_steps=50000 \
-  env.type=libero \
-  env.task=libero_10
+  --policy.path=lerobot/VLA-JEPA-Pretrain \
+  --policy.repo_id=your_org/your_repo \
+  --dataset.repo_id=HuggingFaceVLA/libero \
+  --steps=30000
 ```
 
 **Evaluating the pretrained LIBERO-10 checkpoint:**
@@ -147,14 +130,11 @@ lerobot-train \
 lerobot-eval \
   --policy.path=lerobot/VLA-JEPA-LIBERO \
   --env.type=libero \
-  --env.task=libero_10 \
-  --env.obs_type=pixels_agent_pos \
-  --eval.n_episodes=500 \
-  --eval.batch_size=10 \
-  --policy.device=cuda
-```
+  --env.task=libero_spatial,libero_object,libero_goal,libero_10 \
+  --eval.n_episodes=10 \
+  --eval.batch_size=5 \
 
-This runs all 10 LIBERO-10 tasks (50 episodes each, 500 total) with the default camera setup (`agentview_image` → `observation.images.image`, `robot0_eye_in_hand_image` → `observation.images.image2`) and the `pixels_agent_pos` obs type that provides both images and robot state.
+```
 
 To evaluate a subset of tasks only:
 
@@ -164,9 +144,8 @@ lerobot-eval \
   --env.type=libero \
   --env.task=libero_10 \
   --env.task_ids='[0,1,2]' \
-  --eval.n_episodes=50 \
+  --eval.n_episodes=10 \
   --eval.batch_size=5 \
-  --policy.device=cuda
 ```
 
 ---
@@ -181,25 +160,32 @@ Set `enable_world_model=False`. Only the Qwen backbone and action head are loade
 
 ```bash
 lerobot-train \
-  policy.path=lerobot/VLA-JEPA-Pretrain \
-  policy.enable_world_model=false \
-  dataset.repo_id=your_org/single_camera_dataset
+  --policy.path=lerobot/VLA-JEPA-Pretrain \
+  --policy.enable_world_model=false \
+  --policy.repo_id=your_org/your_repo \
+  --dataset.repo_id=your_org/single_camera_dataset
 ```
 
 **Option 2 — Reinitialize the predictor input projection**
 
 If you want the JEPA self-supervised signal during fine-tuning, load the checkpoint with `strict=False` and reinitialize `model.video_predictor.predictor_embed` for the new `embed_dim`. All other predictor block weights (attention, MLP, norm, output projection) are camera-count-agnostic and can be reused from the pretrained checkpoint.
 
+**Option 3 - Duplicate frames to match the expected number of cameras**
+A bit more advanced, you would need to change some parts of the code to support that.
+
 ---
 
 ## Citation
 
 ```bibtex
-@misc{vla_jepa_2025,
-  title   = {VLA-JEPA: Vision-Language-Action Model with Joint-Embedding Predictive Architecture},
-  author  = {Gin, Wind and others},
-  year    = {2025},
-  url     = {https://huggingface.co/ginwind/VLA-JEPA},
+@misc{sun2026vlajepaenhancingvisionlanguageactionmodel,
+  title         = {VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model},
+  author        = {Jingwen Sun and Wenyao Zhang and Zekun Qi and Shaojie Ren and Zezhi Liu and Hanxin Zhu and Guangzhong Sun and Xin Jin and Zhibo Chen},
+  year          = {2026},
+  eprint        = {2602.10098},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.RO},
+  url           = {https://arxiv.org/abs/2602.10098},
 }
 ```
 
@@ -207,4 +193,4 @@ If you want the JEPA self-supervised signal during fine-tuning, load the checkpo
 
 ## License
 
-Weights are distributed under the license terms of the original [ginwind/VLA-JEPA](https://huggingface.co/ginwind/VLA-JEPA) repository. The LeRobot integration code follows the **Apache 2.0 License**.
+Weights are distributed under the license terms of the original [ginwind/VLA-JEPA](https://huggingface.co/ginwind/VLA-JEPA) repository (**Apache 2.0 License**). The LeRobot integration code follows the **Apache 2.0 License**.