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title: π₀.₅ (Pi05)
- local: molmoact2
title: MolmoAct2
- local: vla_jepa
title: VLA-JEPA
- local: eo1
title: EO-1
- local: groot

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# 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.
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](https://huggingface.co/ginwind/VLA-JEPA).
---
## 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, converted from [ginwind/VLA-JEPA](https://huggingface.co/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](#fine-tuning-on-a-different-embodiment)) |
---
## 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 \
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 \
--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 \
--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` |
```bash
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.
```bash
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:**
```bash
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:
```bash
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.
```bash
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.
At inference time only the Qwen backbone and action head are used; the world model is not needed.
---

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# 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
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`](https://huggingface.co/collections/lerobot/vla-jepa), converted from [ginwind/VLA-JEPA](https://huggingface.co/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](#fine-tuning-on-a-different-embodiment)) |
---
## 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 \
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 \
--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`:
```bash
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` |
```bash
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.
```bash
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:**
```bash
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:
```bash
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.
```bash
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
```bibtex
@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](https://huggingface.co/ginwind/VLA-JEPA) repository (**Apache 2.0 License**). The LeRobot integration code follows the **Apache 2.0 License**.