You can download the model then run the inference scipts in https://github.com/Alibaba-NLP/WebAgent.
WebSailor is a complete post-training methodology designed to teach LLM agents sophisticated reasoning for complex web navigation and information-seeking tasks. It addresses the challenge of extreme uncertainty in vast information landscapes, a capability where previous open-source models lagged behind proprietary systems.
We classify information-seeking tasks into three difficulty levels, where Level 3 represents problems with both high uncertainty and a complex, non-linear path to a solution. To generate these challenging tasks, we introduce SailorFog-QA, a novel data synthesis pipeline that constructs intricate knowledge graphs and then applies information obfuscation. This process creates questions with high initial uncertainty that demand creative exploration and transcend simple, structured reasoning patterns.
Our training process begins by generating expert trajectories and then reconstructing the reasoning to create concise, action-oriented supervision signals, avoiding the stylistic and verbosity issues of teacher models. The agent is first given a "cold start" using rejection sampling fine-tuning (RFT) on a small set of high-quality examples to establish a baseline capability. This is followed by an efficient agentic reinforcement learning stage using our Duplicating Sampling Policy Optimization (DUPO) algorithm, which refines the agent's exploratory strategies.
WebSailor establishes a new state-of-the-art for open-source agents, achieving outstanding results on difficult benchmarks like BrowseComp-en and BrowseComp-zh. Notably, our smaller models like WebSailor-7B outperform agents built on much larger backbones, highlighting the efficacy of our training paradigm. Ultimately, WebSailor closes the performance gap to proprietary systems, achieving results on par with agents like Doubao-Search.