WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic
  Data and Scalable Reinforcement Learning
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.
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Kuan Li
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Zhongwang Zhang
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2025-09-19T06:59:03.607Z
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zhongwang zhang
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Huifeng Yin
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Rui Ye
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Yida Zhao
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2025-09-17T15:29:20.549Z
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Yida Zhao
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Liwen Zhang
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Litu Ou
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2025-09-19T06:59:21.800Z
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Litu Ou
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Dingchu Zhang
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Xixi Wu
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2025-09-17T12:47:32.127Z
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Xixi Wu
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Jialong Wu
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2025-09-17T12:47:34.465Z
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Jialong Wu
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callanwu
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Xinyu Wang
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Zile Qiao
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Zhen Zhang
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Yong Jiang
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Pengjun Xie
admin_assigned
2025-09-19T06:59:53.638Z
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pengjun xie
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xpjandy
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Fei Huang
68ca3cd06e0073c09bd1df6a
Jingren Zhou
WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic
  Data and Scalable Reinforcement Learning
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.
WebSailor, a post-training methodology, enhances open-source models with systematic uncertainty reduction, matching proprietary agents' performance in complex information-seeking tasks.
LLM training DeepResearch BrowseComp reasoning pattern high-uncertainty tasks structured sampling information obfuscation RFT cold start agentic RL training Duplicating Sampling Policy Optimization DUPO