title: WebDancer: Towards Autonomous Information Seeking Agency
summary: 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.
paper_authors._id: 6837c03cbbee677da73e6034
paper_authors.name: Jialong Wu
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-29T02:02:37.069Z
paper_authors._id: 644a4fbc2166258fccc664bc
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/8k3b44MbhQiWuo6i8BnYl.jpeg
paper_authors.fullname: Jialong Wu
paper_authors.type: user
paper_authors.user: callanwu
paper_authors._id: 6837c03cbbee677da73e6035
paper_authors.name: Baixuan Li
paper_authors._id: 6837c03cbbee677da73e6036
paper_authors.name: Runnan Fang
paper_authors._id: 6837c03cbbee677da73e6037
paper_authors.name: Wenbiao Yin
paper_authors._id: 6837c03cbbee677da73e6038
paper_authors.name: Liwen Zhang
paper_authors._id: 6837c03cbbee677da73e6039
paper_authors.name: Zhengwei Tao
paper_authors._id: 6837c03cbbee677da73e603a
paper_authors.name: Dingchu Zhang
paper_authors._id: 6837c03cbbee677da73e603b
paper_authors.name: Zekun Xi
paper_authors._id: 6837c03cbbee677da73e603c
paper_authors.name: Yong Jiang
paper_authors._id: 6837c03cbbee677da73e603d
paper_authors.name: Pengjun Xie
paper_authors._id: 6837c03cbbee677da73e603e
paper_authors.name: Fei Huang
paper_authors._id: 6837c03cbbee677da73e603f
paper_authors.name: Jingren Zhou
paper_title: WebDancer: Towards Autonomous Information Seeking Agency
paper_summary: 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.
paper_ai_summary: The paper proposes a framework for building end-to-end agentic information seeking agents through a combination of data construction, trajectory sampling, supervised fine-tuning, and reinforcement learning, showcasing its effectiveness on information seeking benchmarks.
paper_ai_keywords: browsing data construction trajectories sampling supervised fine-tuning reinforcement learning WebDancer GAIA WebWalkerQA