Robotics
Transformers
Safetensors
paligemma
image-text-to-text
vision-language-action
chain-of-thought
embodied-ai
text-generation-inference
Instructions to use yinchenghust/deepthinkvla_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yinchenghust/deepthinkvla_base with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("yinchenghust/deepthinkvla_base") model = AutoModelForImageTextToText.from_pretrained("yinchenghust/deepthinkvla_base") - Notebooks
- Google Colab
- Kaggle
Add model card and metadata
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by nielsr HF Staff - opened
README.md
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---
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library_name: transformers
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pipeline_tag: robotics
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base_model: physical-intelligence/pi0fast_base
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tags:
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- vision-language-action
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- chain-of-thought
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- embodied-ai
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---
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# DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models
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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.
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- **Paper:** [DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models](https://huggingface.co/papers/2511.15669)
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- **Repository:** [https://github.com/OpenBMB/DeepThinkVLA](https://github.com/OpenBMB/DeepThinkVLA)
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## Model Description
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DeepThinkVLA addresses the challenges of integrating Chain-of-Thought (CoT) into VLA models by satisfying two key conditions:
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1. **Decoding Alignment:** It uses a hybrid-attention decoder that pairs causal attention for linguistic reasoning tokens with bidirectional attention for parallel action decoding.
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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.
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The model is initialized from the `pi0-FAST` checkpoint and demonstrates significant performance gains on robotic manipulation benchmarks.
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## Performance
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- **LIBERO:** 97.0% average success rate.
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- **LIBERO-Plus:** 79.0% zero-shot robustness under distribution shifts.
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- **RoboTwin 2.0:** 59.3% success rate, exceeding prior VLA baselines by significant margins.
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## Citation
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If you find this work helpful, please consider citing:
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```bibtex
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@article{yin2025deepthinkvla,
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title={DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models},
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author={Yin, Cheng and Lin, Yankai and Xu, Wang and Tam, Sikyuen and Zeng, Xiangrui and Liu, Zhiyuan and Yin, Zhouping},
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journal={arXiv preprint arXiv:2511.15669},
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year={2025}
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}
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```
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