VLA-JEPA#

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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#

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 directly inside the LeRobot org here: lerobot/VLA-JEPA, 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 (view duplicated ×2)

Enabled

7

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

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)

gripper_dim

6

Index of the gripper dimension in the action vector (e.g. 6 for a 7-DoF arm with gripper as the last joint)

gripper_threshold

0.5

Threshold used by pre_snap_gripper_action and binarize_gripper_action to binarize the gripper dimension

pre_snap_gripper_action

True

Snap the gripper dim to {0, 1} before unnormalization. Set to False for robots without a binary gripper

binarize_gripper_action

True

Binarize the gripper dim to {-1, 1} after unnormalization. Set to False for robots without a binary gripper

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 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%

Fine-tuning on datasets with a different number of cameras#

The pretrained world model predictor was trained with embed_dim = jepa_tubelet_size × 1024 (default jepa_tubelet_size=2).

Default behaviour — view padding / trimming (no action required)

When fine-tuning from VLA-JEPA-Pretrain the model automatically adjusts the number of views fed to the world model to match jepa_tubelet_size:

  • Single-view datasets (e.g. BridgeV2): the single-view latent is duplicated to produce a two-view world-model input, preserving the JEPA self-supervised signal without any weight mismatch.

  • >2-view datasets (e.g. DROID with 3 views): all views are passed to the Qwen backbone (for richer context), but only the first jepa_tubelet_size views (one wrist + one third-person, following the configured view order) are used for the world model.

Option 1 — Disable the world model

Set enable_world_model=False to skip the JEPA loss entirely. Only the Qwen backbone and action head are loaded and trained. This 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 to change jepa_tubelet_size to a value other than 2, 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{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},
}

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.