WebDancer: Towards Autonomous Information Seeking Agency
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.
6837c03cbbee677da73e6034
Jialong Wu
extracted_confirmed
2025-07-04T08:14:49.966Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/8k3b44MbhQiWuo6i8BnYl.jpeg
Jialong Wu
user
callanwu
6837c03cbbee677da73e6035
Baixuan Li
6837c03cbbee677da73e6036
Runnan Fang
6837c03cbbee677da73e6037
Wenbiao Yin
6837c03cbbee677da73e6038
Liwen Zhang
6837c03cbbee677da73e6039
Zhengwei Tao
6837c03cbbee677da73e603a
Dingchu Zhang
6837c03cbbee677da73e603b
Zekun Xi
6837c03cbbee677da73e603c
Yong Jiang
6837c03cbbee677da73e603d
Pengjun Xie
6837c03cbbee677da73e603e
Fei Huang
6837c03cbbee677da73e603f
Jingren Zhou
WebDancer: Towards Autonomous Information Seeking Agency
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.
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.
browsing data construction trajectories sampling supervised fine-tuning reinforcement learning WebDancer GAIA WebWalkerQA