README.md

---
license: mit
library_name: transformers
pipeline_tag: text-generation
---

# 🌐 WebThinker-R1-14B

<div align="left" style="line-height: 1;">
  <a href="https://github.com/RUC-NLPIR/WebThinker" target="_blank" style="margin: 2px;">
    <img alt="GitHub" src="https://img.shields.io/badge/GitHub-WebThinker-blue?logo=github" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://arxiv.org/abs/2504.21776" target="_blank" style="margin: 2px;">
    <img alt="Paper" src="https://img.shields.io/badge/Paper-arXiv-b5212f.svg?logo=arxiv" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://huggingface.co/papers/2504.21776" target="_blank" style="margin: 2px;">
    <img alt="Paper" src="https://img.shields.io/badge/Paper-Hugging%20Face-yellow?logo=huggingface" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://opensource.org/licenses/MIT" target="_blank" style="margin: 2px;">
    <img alt="License" src="https://img.shields.io/badge/LICENSE-MIT-green.svg" style="display: inline-block; vertical-align: middle;"/>
  </a>
</div>

## WebThinker: Empowering Large Reasoning Models with Deep Research Capability

Large reasoning models (LRMs), such as OpenAI-o1 and DeepSeek-R1, demonstrate
impressive long-horizon reasoning capabilities. However, their reliance on
static internal knowledge limits their performance on complex,
knowledge-intensive tasks and hinders their ability to produce comprehensive
research reports requiring synthesis of diverse web information. To address
this, we propose WebThinker, a deep research agent that empowers LRMs
to autonomously search the web, navigate web pages, and draft research reports
during the reasoning process. WebThinker integrates a Deep Web
Explorer module, enabling LRMs to dynamically search, navigate, and extract
information from the web when encountering knowledge gaps. It also employs an
Autonomous Think-Search-and-Draft strategy, allowing the model to
seamlessly interleave reasoning, information gathering, and report writing in
real time. To further enhance research tool utilization, we introduce an
RL-based training strategy via iterative online Direct Preference
Optimization (DPO). Extensive experiments on complex reasoning benchmarks
(GPQA, GAIA, WebWalkerQA, HLE) and scientific report generation tasks (Glaive)
demonstrate that WebThinker significantly outperforms existing methods and
strong proprietary systems. Our approach enhances LRM reliability and
applicability in complex scenarios, paving the way for more capable and
versatile deep research systems.


## Overview

WebThinker-R1-14B 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 14B parameter model provides deep research capabilities through:

- **Deep Web Exploration**: 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 report generation, 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) (this model)
- [WebThinker-R1-32B](https://huggingface.co/lixiaoxi45/WebThinker-R1-32B)

## Usage

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

## 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 MIT License.

## Contact

For any questions or feedback, please reach out to us at [xiaoxi_li@ruc.edu.cn](mailto:xiaoxi_li@ruc.edu.cn).