WebDancer: Towards Autonomous Information Seeking Agency
Authors: name: Jialong Wu, name: Baixuan Li, name: Runnan Fang, name: Wenbiao Yin, name: Liwen Zhang, name: Zhengwei Tao, name: Dingchu Zhang, name: Zekun Xi, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou

Addressing intricate real-world problems necessitates in-depth information
seeking and multi-step reasoning. Recent progress in agentic systems,
exemplified by Deep Research, underscores the potential for autonomous
multi-step research. In this work, we present a cohesive paradigm for building
end-to-end agentic information seeking agents from a data-centric and
training-stage perspective. Our approach consists of four key stages: (1)
browsing data construction, (2) trajectories sampling, (3) supervised
fine-tuning for effective cold start, and (4) reinforcement learning for
enhanced generalisation. We instantiate this framework in a web agent based on
the ReAct, WebDancer. Empirical evaluations on the challenging information
seeking benchmarks, GAIA and WebWalkerQA, demonstrate the strong performance of
WebDancer, achieving considerable results and highlighting the efficacy of our
training paradigm. Further analysis of agent training provides valuable
insights and actionable, systematic pathways for developing more capable
agentic models. The codes and demo will be released in
https://github.com/Alibaba-NLP/WebAgent.