Poster Sat, Apr 25, 2026 • 11:15 AM – 1:45 PM PDT Pavilion 4 P4-#5001

WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning

Kuan Li ⋅ Zhongwang Zhang ⋅ Huifeng Yin ⋅ Rui Ye ⋅ Yida Zhao ⋅ Liwen Zhang ⋅ Litu Ou ⋅ Ding-Chu Zhang ⋅ Xixi Wu ⋅ Xinmiao Yu ⋅ Jialong Wu ⋅ Xinyu Wang ⋅ Zile Qiao ⋅ Zhen Zhang ⋅ Yong Jiang ⋅ Pengjun Xie ⋅ Fei Huang ⋅ Zhiqin Xu ⋅ Shuai Wang ⋅ Minhao Cheng ⋅ Jingren Zhou

Abstract

To significantly advance the capabilities of open-source web agents, we present WebSailor-V2, a complete post-training pipeline encompassing data construction, Supervised Fine-Tuning (SFT), and Reinforcement Learning (RL). Our methodology features two key innovations: (1) On the data front, we developed SailorFog-QA-2, a novel dataset built from a densely interconnected knowledge graph that introduces a wide variety of uncertainties beyond simple obfuscation, fostering more sophisticated reasoning. (2) For training, we engineered a dual-environment RL framework, combining a high-fidelity simulator for rapid, low-cost algorithmic iteration with a robust, managed real-world environment for stable final policy training, all integrated within a symbiotic data-policy feedback loop. Trained on the Qwen3-30B-A3B model, WebSailor-V2 achieves state-of-the-art results, scoring 35.3 on BrowseComp-EN, 44.1 on BrowseComp-ZH, and 30.6 on Humanity's Last Exam (HLE). Notably, our 30B-A3B MOE agent significantly outperforms all existing open-source agents and surpasses even the 671B DeepSeek-V3.1, demonstrating performance competitive with leading proprietary systems.