💥 💥 💥 Stay tuned for more updates! We are working on the building native agentic model based on Browser and more open-domained environments!
- WebDancer (Preprint 2025) - WebDancer: Towards Autonomous Information Seeking Agency
- WebWalker (ACL 2025) - WebWalker: Benchmarking LLMs in Web Traversal
- Native agentic search reasoning model using ReAct framework towards autonomous information seeking agency and Deep Research-like model.
- We introduce a four-stage training paradigm comprising browsing data construction, trajectory sampling, supervised fine-tuning for effective cold start, and reinforcement learning for improved generalization, enabling the agent to autonomously acquire autonomous search and reasoning skills.
- Our data-centric approach integrates trajectory-level supervision fine-tuning and reinforcement learning (DAPO) to develop a scalable pipeline for training agentic systems via SFT or RL.
- WebDancer achieves a Pass@3 score of 61.1% on GAIA and 54.6% on WebWalkerQA.
We provide demos for WebWalkerQA, GAIA and Daily Use. Our model can execute the long-horizon tasks with multiple steps and complex reasoning, such as web traversal, information seeking and question answering.
⌛️ The deployment of models and demos will be updated soon.
2025.05.29
We release WebDancer, a native agentic search model towards autonomous information seeking agency and Deep Research-like model.2025.05.15
WebWalker is accepted by ACL 2025 main conference.2025.01.14
We relaese WebWalker, a benchmark for LLMs in web traversal and a multi-agent framework for information seeking.
The content of this project itself is licensed under LICENSE.
If this work is helpful, please kindly cite as:
@misc{wu2025webdancer,
title={WebDancer: Towards Autonomous Information Seeking Agency},
author={Jialong Wu and Baixuan Li and Runnan Fang and Wenbiao Yin and Liwen Zhang and Zhengwei Tao and Dingchu Zhang and Zekun Xi and Yong Jiang and Pengjun Xie and Fei Huang and Jingren Zhou},
year={2025},
eprint={2505.22648},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.22648},
}
@misc{wu2025webwalker,
title={WebWalker: Benchmarking LLMs in Web Traversal},
author={Jialong Wu and Wenbiao Yin and Yong Jiang and Zhenglin Wang and Zekun Xi and Runnan Fang and Deyu Zhou and Pengjun Xie and Fei Huang},
year={2025},
eprint={2501.07572},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.07572},
}
The repo is contributed by Jialong Wu, if you have any questions, please feel free to contact via [email protected] or [email protected] or create an issue.