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WebThinker-R1-32B
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# 🌐 WebThinker-R1-32B

[![Hugging Face](https://img.shields.io/badge/Hugging_Face-WebThinker--R1--32B-yellow?logo=huggingface)](https://huggingface.co/lixiaoxi45/WebThinker-R1-32B)
[![Paper](https://img.shields.io/badge/Paper-arXiv-b5212f.svg?logo=arxiv)](https://arxiv.org/abs/2504.21776)
[![License](https://img.shields.io/badge/LICENSE-Apache_2.0-green.svg)](https://opensource.org/licenses/Apache-2.0)

## Overview

WebThinker-R1-32B is part of the WebThinker series that enables large reasoning models to autonomously search, explore web pages, and draft research reports within their thinking process. This 32B parameter model provides deep research capabilities through:

- **Deep Web Explorer**: Enables autonomous web searches and page navigation by clicking interactive elements to extract relevant information while maintaining reasoning coherence
- **Autonomous Think-Search-and-Draft**: Integrates real-time knowledge seeking with content creation, allowing the model to draft sections as information is gathered
- **RL-based Training**: Leverages iterative online DPO training with preference pairs constructed from reasoning trajectories to optimize end-to-end performance

## Related Models

- [WebThinker-QwQ-32B](https://huggingface.co/lixiaoxi45/WebThinker-QwQ-32B)
- [WebThinker-R1-7B](https://huggingface.co/lixiaoxi45/WebThinker-R1-7B)
- [WebThinker-R1-14B](https://huggingface.co/lixiaoxi45/WebThinker-R1-14B)
- [WebThinker-R1-32B](https://huggingface.co/lixiaoxi45/WebThinker-R1-32B) (this model)

## Usage

This model can be used for:
- Complex problem solving requiring external knowledge
- Scientific research report generation
- Open-ended reasoning tasks

## Resources

- [GitHub Repository](https://github.com/RUC-NLPIR/WebThinker)
- [Paper](https://arxiv.org/abs/2504.21776)

## Citation

```bibtex
@article{Li2025WebThinker,
  author       = {Xiaoxi Li and
                  Jiajie Jin and
                  Guanting Dong and
                  Hongjin Qian and
                  Yutao Zhu and
                  Yongkang Wu and
                  Ji{-}Rong Wen and
                  Zhicheng Dou},
  title        = {WebThinker: Empowering Large Reasoning Models with Deep Research Capability},
  journal      = {CoRR},
  volume       = {abs/2504.21776},
  year         = {2025},
  url          = {https://arxiv.org/abs/2504.21776},
  doi          = {10.48550/ARXIV.2504.21776},
  eprinttype    = {arXiv},
  eprint       = {2504.21776}
}
```

## License

This model is released under the Apache License 2.0.

## Contact

For any questions or feedback, please reach out to us at [[email protected]](mailto:[email protected]).