lerobot-clone/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.

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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

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).

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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.

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Configuration

Key parameters in VLAJEPAConfig:

Parameter	Default	Description
chunk_size	7	Number of actions predicted per inference call
n_action_steps	7	Steps executed from the predicted chunk before re-planning
num_video_frames	8	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	4	Euler integration steps for action denoising
freeze_qwen	False	Freeze the Qwen3-VL backbone and only train the action head
reinit_modules	None	Key prefixes allowed to be randomly re-initialised on load (for cross-embodiment transfer, see Fine-tuning on a different embodiment)

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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

lerobot-train \
  policy.type=vla_jepa \
  policy.repo_id=your_org/your_repo \
  dataset.repo_id=your_org/your_dataset

Fine-tuning from a pretrained checkpoint

lerobot-train \
  --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:

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

Fine-tuning on a different embodiment

When the target robot has a different action or state dimensionality than the pretrained checkpoint, the input/output projection layers of the action head will have mismatched shapes and cannot be loaded directly. reinit_modules lets you list the key prefixes that are allowed to mismatch — those layers are randomly re-initialised while every other weight is reused from the checkpoint. Any shape mismatch outside the listed prefixes raises an error.

The layers that depend on action_dim and state_dim are:

Layer	Key prefix
Action encoder (action_dim → inner_dim)	model.action_model.action_encoder
Action decoder (hidden_size → action_dim)	model.action_model.action_decoder
State encoder (state_dim → inner_dim)	model.action_model.state_encoder

lerobot-train \
  --policy.path=lerobot/VLA-JEPA-Pretrain \
  --policy.repo_id=your_org/your_repo \
  --policy.freeze_qwen=true \
  --policy.reinit_modules='["model.action_model.action_encoder", "model.action_model.action_decoder", "model.action_model.state_encoder"]' \
  --dataset.repo_id=your_org/your_dataset

If your robot has no proprioceptive state, omit model.action_model.state_encoder from the list.

Reproducing the LIBERO results

Training on LIBERO: 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.

lerobot-train \
  --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:

lerobot-eval \
  --policy.path=lerobot/VLA-JEPA-LIBERO \
  --env.type=libero \
  --env.task=libero_spatial,libero_object,libero_goal,libero_10 \
  --eval.n_episodes=10 \
  --eval.batch_size=5

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=10 \
  --eval.batch_size=5

Expected results:

Suite	Episodes	Successes	Success Rate
libero_spatial	100	93	95.0%
libero_object	100	100	100.0%
libero_goal	100	98	98.0%
libero_10	100	96	93.0%
Overall	400	387	96.5%

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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 \
  --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.

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Citation

@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},
}

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License

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