---
library_name: transformers
pipeline_tag: robotics
base_model: physical-intelligence/pi0fast_base
tags:
- vision-language-action
- chain-of-thought
- embodied-ai
---

# DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models

DeepThinkVLA is a Vision-Language-Action (VLA) model designed to enhance the reasoning capabilities of robotic agents through explicit deliberation. It refactors the policy into a 2.9B parameter hybrid decoder that generates a reasoning trace (Chain-of-Thought) before emitting action chunks.

- **Paper:** [DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models](https://huggingface.co/papers/2511.15669)
- **Repository:** [https://github.com/OpenBMB/DeepThinkVLA](https://github.com/OpenBMB/DeepThinkVLA)

## Model Description
DeepThinkVLA addresses the challenges of integrating Chain-of-Thought (CoT) into VLA models by satisfying two key conditions:
1. **Decoding Alignment:** It uses a hybrid-attention decoder that pairs causal attention for linguistic reasoning tokens with bidirectional attention for parallel action decoding.
2. **Causal Alignment:** The model is trained via a two-stage SFT-then-RL pipeline (using GRPO) to ensure the reasoning chain is causally linked to task success.

The model is initialized from the `pi0-FAST` checkpoint and demonstrates significant performance gains on robotic manipulation benchmarks.

## Performance
- **LIBERO:** 97.0% average success rate.
- **LIBERO-Plus:** 79.0% zero-shot robustness under distribution shifts.
- **RoboTwin 2.0:** 59.3% success rate, exceeding prior VLA baselines by significant margins.

## Citation
If you find this work helpful, please consider citing:

```bibtex
@article{yin2025deepthinkvla,
  title={DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models},
  author={Yin, Cheng and Lin, Yankai and Xu, Wang and Tam, Sikyuen and Zeng, Xiangrui and Liu, Zhiyuan and Yin, Zhouping},
  journal={arXiv preprint arXiv:2511.15669},
  year={2025}
}
```