WebSailor: Navigating Super-human Reasoning for Web Agent
Authors: name: Kuan Li; name: Zhongwang Zhang; name: Huifeng Yin; name: Liwen Zhang; name: Litu Ou; name: Jialong Wu; name: Wenbiao Yin; name: Baixuan Li; name: Zhengwei Tao; name: Xinyu Wang; name: Weizhou Shen; name: Junkai Zhang; name: Dingchu Zhang; name: Xixi Wu; name: Yong Jiang; name: Ming Yan; name: Pengjun Xie; name: Fei Huang; name: Jingren Zhou
Published: 2025-07-03 12:59:07

Abstract: Transcending human cognitive limitations represents a critical frontier in
LLM training. Proprietary agentic systems like DeepResearch have demonstrated
superhuman capabilities on extremely complex information-seeking benchmarks
such as BrowseComp, a feat previously unattainable. We posit that their success
hinges on a sophisticated reasoning pattern absent in open-source models: the
ability to systematically reduce extreme uncertainty when navigating vast
information landscapes. Based on this insight, we introduce WebSailor, a
complete post-training methodology designed to instill this crucial capability.
Our approach involves generating novel, high-uncertainty tasks through
structured sampling and information obfuscation, RFT cold start, and an
efficient agentic RL training algorithm, Duplicating Sampling Policy
Optimization (DUPO). With this integrated pipeline, WebSailor significantly
outperforms all opensource agents in complex information-seeking tasks,
matching proprietary agents' performance and closing the capability gap.