lerobot/docs/source/policy_vla_jepa_README.md

VLA-JEPA

This is the LeRobot port of VLA-JEPA, a Vision-Language-Action model that combines a Qwen3-VL language backbone with a self-supervised video world model (V-JEPA2) and a flow-matching DiT action head.

---

Architecture Overview

VLA-JEPA has three main components:

Component	Module	Role
Qwen3-VL backbone	Qwen3VLInterface	Fuses images + language instruction into context tokens
DiT-B action head	VLAJEPAActionHead	Flow-matching diffusion over the action chunk
V-JEPA2 world model	ActionConditionedVideoPredictor	Self-supervised video prediction loss (training only)

Data flow

Training:

1. A video clip of num_video_frames frames is encoded by V-JEPA2 into per-frame patch tokens.
2. The Qwen3-VL backbone processes multi-view images + the task instruction and produces a sequence of context tokens that includes special action tokens (for world model conditioning) and embodied tokens.
3. The action head receives those context tokens as cross-attention keys/values and predicts a denoised action chunk via flow matching.
4. The world model predictor uses the action tokens extracted from Qwen to predict future V-JEPA2 frame embeddings; a regression loss on those predictions is added to the action loss.

Inference: Only Qwen + the action head are used. The world model is not needed at inference time.

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
DiT-B	768	12	64
DiT-L	1536	32	48

World model details

The video predictor is a ViT-style transformer (ActionConditionedVideoPredictor) that takes:

• Frame tokens: V-JEPA2 patch embeddings projected to predictor_embed_dim
• Action tokens: Qwen action token embeddings projected to predictor_embed_dim

It uses block-causal attention so each temporal step can attend to all previous steps. The predictor's input embed_dim equals num_views × video_encoder_hidden_size (e.g. 2 views × 1024 = 2048 for the pretrained checkpoints).

---

Pretrained Checkpoints

Three checkpoints are available, converted from ginwind/VLA-JEPA:

Checkpoint	Dataset	Cameras	World model	Action dim
lerobot/VLA-JEPA-LIBERO	LIBERO-10	2 (agentview + wrist)	Enabled	7
lerobot/VLA-JEPA-Pretrain	DROID 1.0.1	2 (exterior left views)	Enabled	7
lerobot/VLA-JEPA-SimplerEnv	OXE Bridge / RT-1	1	Disabled*	7

* The SimplerEnv checkpoint was fine-tuned from Pretrain. The world model predictor architecture expects embed_dim=2048 (2-camera input) but SimplerEnv is single-camera, so the world model cannot be loaded cleanly. Since inference only needs Qwen + the action head, enable_world_model=False is set for this variant. See Fine-tuning on single-camera datasets for implications.

All checkpoints use Qwen/Qwen3-VL-2B-Instruct as the language backbone.

Loading a pretrained checkpoint

from lerobot.policies.vla_jepa.modeling_vla_jepa import VLAJEPAPolicy

policy = VLAJEPAPolicy.from_pretrained("lerobot/VLA-JEPA-LIBERO")

---

Configuration

Key parameters in VLAJEPAConfig:

Parameter	Default	Description
chunk_size	16	Number of actions predicted per inference call
n_action_steps	16	Steps executed from the predicted chunk before re-planning
num_video_frames	16	Video clip length fed to the world model
enable_world_model	True	Whether to load and train the V-JEPA2 predictor
world_model_loss_weight	0.1	Weight of the JEPA prediction loss relative to the action loss
num_inference_timesteps	10	Euler integration steps for action denoising
freeze_qwen	False	Freeze the Qwen3-VL backbone and only train the action head

---

Training

Full training from scratch

lerobot-train \
  dataset.repo_id=your_org/your_dataset \
  policy.chunk_size=16 \
  policy.n_action_steps=16

Fine-tuning from a pretrained checkpoint

lerobot-train \
  policy.path=lerobot/VLA-JEPA-Pretrain \
  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:

lerobot-train \
  policy.path=lerobot/VLA-JEPA-Pretrain \
  dataset.repo_id=your_org/your_dataset \
  policy.freeze_qwen=true

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

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

Evaluating the pretrained LIBERO-10 checkpoint:

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

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:

lerobot-eval \
  --policy.path=lerobot/VLA-JEPA-LIBERO \
  --env.type=libero \
  --env.task=libero_10 \
  --env.task_ids='[0,1,2]' \
  --eval.n_episodes=50 \
  --eval.batch_size=5 \
  --policy.device=cuda

---

Fine-tuning on single-camera datasets

The pretrained world model predictor was trained with embed_dim = num_views × 1024. If your target dataset has fewer cameras than the source checkpoint, the predictor input projection will have a shape mismatch and cannot be loaded.

Option 1 — Disable the world model (recommended)

Set enable_world_model=False. Only the Qwen backbone and action head are loaded and trained. This matches the original SimplerEnv fine-tuning strategy and is sufficient for good action performance.

lerobot-train \
  policy.path=lerobot/VLA-JEPA-Pretrain \
  policy.enable_world_model=false \
  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.

---

Citation

@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},
}

---

License

Weights are distributed under the license terms of the original ginwind/VLA-JEPA repository. The LeRobot integration code follows the Apache 2.0 License.