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* first commit * feat(policies): add VLA-JEPA * feat(policies): add VLA-JEPA * support vla_jepa * (feat)policies: add VLA-JEPA * linting * adding deps to pyproject.toml * updating uv lock * adding guards to avoid needing transformers and diffusers for type checking and basic tests * fixing action and state dim * fix warnings with qwen processor kwargs * fixing wm_loss not propagating * adjusting obs steps, tublets size to match original implementation * some more fixes to be closer to the original implem * adding more tests to ensure good coverage * align VLA-JEPA architecture with original checkpoint - Remove stale `action_num_heads` / `action_attention_head_dim` config fields; DiT head dimensions are now always derived from the preset (DiT-B/L/test). - Add `num_target_vision_tokens` and `action_max_seq_len` config fields required by the action head's future-token embedding and positional embedding tables. - Fix default `qwen_model_name` to 2B (matches all released checkpoints). - Rename `ActionEncoder` attrs w1/w2/w3 → layer1/layer2/layer3 to match checkpoint key names; replace `nn.Sequential` decoder/state-encoder with `_MLP2` (layer1/layer2 naming). - Fix `VLAJEPAActionHead` to size ActionEncoder and StateEncoder at `inner_dim` (DiT input width) rather than `action_hidden_size` (DiT output width). - Rename `DiT.blocks` → `transformer_blocks` and `attn` → `attn1` to match checkpoint; add alternating cross/self attention (even blocks cross-attend to Qwen context, odd blocks self-attend). - Add `DiT-test` preset for unit tests. - Rewrite `ActionConditionedVideoPredictor` with explicit ViT-style blocks (`_PredictorBlock` with fused qkv) to match checkpoint structure; rename `encoder`/`norm`/`proj` → `predictor_blocks`/`predictor_norm`/`predictor_proj`. * propagate action_is_pad masking through VLA-JEPA policy pipeline Pass the `action_is_pad` tensor from the batch through to the action head so padded timesteps are excluded from the flow-matching loss. * update VLA-JEPA tests for arch changes and action_is_pad - Switch conftest to use `action_model_type="DiT-test"` now that `action_num_heads` / `action_attention_head_dim` have been removed. - Add action_head tests covering fully-padded loss (zero) and equivalence of action_is_pad=None vs all-zeros mask. - Remove obsolete `test_native_to_lerobot_wm_only` test. * add VLA-JEPA documentation Covers architecture overview, pretrained checkpoints, config reference, training/eval commands for LIBERO-10, and guidance on fine-tuning for single-camera datasets. * add one-shot script to convert ginwind/VLA-JEPA checkpoints to safetensors (will remove once migrated) * make default params more aligned with paper and pretrained models - adding possibility of freezing qwen backbone and world model - added tests for weight loading * trying out to re-init the action head to avoid pretraining dimension mismatch * allow different state dim and action dim * removing missleading future_action_window_size to just use chunk_size * lots of changes to make existing weights work, need to massively refactor the pre and post processing * refactoring into using pre and post processor * pre-commit cleanup * fixing doc defaults args Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> * adressing dtype zeros issue * adding guard for diffusers * fixing training and exal examples * trying to close success rate gap * fix qwen norm layer output libero eval is now as expected * adding instructions for different embodiement + fixing some tests * smol fix to avoid having default CPU device when training * fixing misconception about multiview / singleview handling * removing conversion script * adding licences * adding .mdx docs and shortening polivy_vla_jepa_README.md * removing useless pre-processor * cleanup * removing swish in favor of silu * adding configuration gripper index and threshold * fixing simlink --------- Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> Co-authored-by: ginwind <ginwind@mail.ustc.edu.cn>
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VLA-JEPA
This repository contains 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.
Converted from ginwind/VLA-JEPA.
Architecture Overview
| 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) |
At inference time only the Qwen backbone and action head are used; the world model is not needed.
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.