Title: WebSeer: Training Deeper Search Agents through Reinforcement Learning with Self-Reflection
Primary area: foundation or frontier models, including LLMs
Keywords: lm agent, retrieval-augmented generation, Reinforcement Learning

Abstract: Search agents have achieved significant advancements in enabling intelligent information retrieval and decision-making within interactive environments.
Although reinforcement learning has been employed to train agentic models capable of more dynamic interactive retrieval, existing methods are limited by shallow tool-use depth and the accumulation of errors over multiple iterative interactions.
In this paper, we present WebSeer, a more intelligent search agent trained via reinforcement learning enhanced with a self-reflection mechanism. Specifically, we construct a large dataset annotated with reflection patterns and design a two-stage training framework that unifies cold start and reinforcement learning within the self-reflection paradigm for real-world web-based environments, which enables the model to generate longer and more reflective tool-use trajectories. 
Our approach substantially extends tool-use chains and improves answer accuracy. Using a single 14B model, we achieve state-of-the-art results on HotpotQA and SimpleQA, with accuracies of 72.3\% and 90.0\%, respectively, and demonstrate strong generalization to out-of-distribution datasets.