WebSailor is a groundbreaking post-training framework developed by Alibaba's Tongyi Lab that unlocks superhuman web reasoning capabilities in open-source agents. This innovative framework bridges the gap between open-source and proprietary web agents by instilling sophisticated reasoning in language agents, enabling them to tackle complex tasks with high uncertainty.
WebSailor's approach involves structured sampling, information obfuscation, and an efficient reinforcement learning algorithm called Duplicating Sampling Policy Optimization (DUPO). This methodology allows language models to successfully complete extremely complex tasks previously considered unsolvable. The framework's two-stage training process, which includes an RFT cold start stage followed by DUPO, enables effective and efficient training of language models.
The effectiveness of WebSailor is demonstrated by its impressive performance on benchmarks like BrowseComp-en, BrowseComp-zh, and GAIA. WebSailor-72B achieved scores of 12.0% on BrowseComp-en, 30.1% on BrowseComp-zh, and 55.4% on GAIA, significantly outperforming all open-source agents and frameworks while closing the performance gap with leading proprietary systems.
WebSailor's capabilities have significant implications for the development of AI agents. Its ability to handle complex information-seeking tasks and provide accurate answers makes it a valuable tool for applications that require sophisticated reasoning and decision-making. By leveraging WebSailor, developers can create more intelligent and capable AI agents that can tackle challenging tasks with ease.
