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RynnVLA-002
Alibaba-DAMO-Academy/RynnVLA-002
RynnVLA-002 is a machine learning model from Alibaba-DAMO-Academy. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
<p align="center" <img src="rynnvla-002/assets/logo.png?raw=true" width="80" style="margin-bottom: 0.1;"/ <p
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Updated Nov 22, 2025
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169 GB
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11
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At a glance
- Access
- Public
- Created
- Nov 20, 2025
- Updated
- Nov 22, 2025
- SHA
- be44787b
Model card
From the Hugging Face model README
<p align="center">
<img src="rynnvla-002/assets/logo.png?raw=true" width="80" style="margin-bottom: 0.1;"/>
<p>
<h3 align="center"><a href="" style="color:#9C276A">
RynnVLA-002: A Unified Vision-Language-Action and World Model</a></h3>
<h5 align="center"> If our project helps you, please give us a star ⭐ on GitHub to support us. 🙏🙏 </h2>
<h5 align="center">
🌟 Introduction
RynnVLA-002 is an autoregressive action world model that unifies action and image understanding and generation. RynnVLA-002 intergrates Vision-Language-Action (VLA) model (action model) and world model in one single framework. Compared to WorldVLA, RynnVLA-002 adds a continous Action Transformer, wrist camera input and generation, and state input. RynnVLA-002 achieves 97.4% success rate on LIBERO benchmark.
<div style="text-align: center;"> <img src="rynnvla-002/assets/overview.png" style="max-width: 100%; height: auto; display: block; margin: 0 auto;"> </div> <br>Model Zoo
VLA Model (256 * 256)
| Model | HF Link | Continous Action SR (%) | Discrete Action SR (%) |
|---|---|---|---|
| LIBERO-Spatial | Alibaba-DAMO-Academy/RynnVLA-002/VLA_model_256/libero_spatial | 99.0 | 94.2 |
| LIBERO-Object | Alibaba-DAMO-Academy/RynnVLA-002/VLA_model_256/libero_object | 99.8 | 96.8 |
| LIBERO-Goal | Alibaba-DAMO-Academy/RynnVLA-002/VLA_model_256/libero_goal | 96.4 | 94.6 |
| LIBERO-Long | Alibaba-DAMO-Academy/RynnVLA-002/VLA_model_256/libero_10 | 94.4 | 87.6 |
World Model (512 * 512)
| Goal | HF Link | FVD↓ | PSNR↑ | SSIM↑ | LPIPS↓ |
|---|---|---|---|---|---|
| World Model | Alibaba-DAMO-Academy/RynnVLA-002/World_model_512/libero_goal | 370.0 | 22.25 | 77.84 | 19.70 |
| Action World Model | Alibaba-DAMO-Academy/RynnVLA-002/Action_World_model_512/libero_goal | 336.8 | 22.13 | 78.13 | 19.43 |
| Object | HF Link | FVD↓ | PSNR↑ | SSIM↑ | LPIPS↓ |
|---|---|---|---|---|---|
| World Model | Alibaba-DAMO-Academy/RynnVLA-002/World_model_512/libero_object | 1141.6 | 20.31 | 59.59 | 27.30 |
| Action World Model | Alibaba-DAMO-Academy/RynnVLA-002/Action_World_model_512/libero_object | 877.2 | 22.18 | 65.03 | 22.60 |
| Spatial | HF Link | FVD↓ | PSNR↑ | SSIM↑ | LPIPS↓ |
|---|---|---|---|---|---|
| World Model | Alibaba-DAMO-Academy/RynnVLA-002/World_model_512/libero_spatial | 405.4 | 22.32 | 79.15 | 20.28 |
| Action World Model | Alibaba-DAMO-Academy/RynnVLA-002/Action_World_model_512/libero_spatial | 373.1 | 23.88 | 82.41 | 16.33 |
| Long | HF Link | FVD↓ | PSNR↑ | SSIM↑ | LPIPS↓ |
|---|---|---|---|---|---|
| World Model | Alibaba-DAMO-Academy/RynnVLA-002/World_model_512/libero_10 | 557.73 | 18.24 | 69.16 | 31.60 |
| Action World Model | Alibaba-DAMO-Academy/RynnVLA-002/Action_World_model_512/libero_10 | 427.86 | 19.36 | 72.19 | 27.78 |
License <a name="license"></a>
All assets and code are under the Apache 2.0 license unless specified otherwise.
Citation <a name="citation"></a>
If you find the project helpful for your research, please consider citing our paper:
@article{cen2025WorldVLA,
title={WorldVLA: Towards Autoregressive Action World Model},
author={Cen, Jun and Yu, Chaohui and Yuan, Hangjie and Jiang, Yuming and Huang, Siteng and Guo, Jiayan and Li, Xin and Song, Yibing and Luo, Hao and Wang, Fan and Zhao, Deli and Chen, Hao},
journal={arXiv preprint arXiv:},
year={2025}
}
RynnVLA-001: A Vision-Language-Action Model Boosted by Generative Priors <br> Yuming Jiang, Siteng Huang, Shengke Xue, Yaxi Zhao, Jun Cen, Sicong Leng, Jiayan Guo, Kexiang Wang, Kehan Li, Mingxiu Chen, Fan Wang, Deli Zhao, Xin Li <br>
<be>
RynnEC: Bringing MLLMs into Embodied World <br> Ronghao Dang*, Yuqian Yuan*, Yunxuan Mao*, Kehan Li*, Jiangpin Liu, Zhikai Wang, Fan Wang, Deli Zhao, Xin Li <br>
</p></details>RynnRCP: Open Robotics Context Protocol and RobotMotion <br> RynnBot Team <br>
<br>
Acknowledgment <a name="acknowledgment"></a>
This project builds upon Lumina-mGPT, Chemeleon, and OpenVLA. We thank these teams for their open-source contributions.
- Created
- Nov 20, 2025
- Updated
- Nov 22, 2025