WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic
  Data and Scalable Reinforcement Learning
Authors: name: Kuan Li, name: Zhongwang Zhang, name: Huifeng Yin, name: Rui Ye, name: Yida Zhao, name: Liwen Zhang, name: Litu Ou, name: Dingchu Zhang, name: Xixi Wu, name: Jialong Wu, name: Xinyu Wang, name: Zile Qiao, name: Zhen Zhang, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou

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 open-source agents in complex information-seeking tasks,
matching proprietary agents' performance and closing the capability gap.