SAAS: Self-Aware Reinforcement Learning for Over-Search Mitigation in Agentic Search

The article presents SAAS, a novel reinforcement learning framework aimed at mitigating over-search in agentic search systems used by LLMs. SAAS features a search boundary modeling mechanism, a boundary-aware reward module, and a stage-wise optimization strategy, which collectively enhance self-awareness in agents, allowing them to regulate search behavior effectively while maintaining accuracy. This development is significant for practitioners as it addresses the inefficiencies associated with excessive searches, thus reducing inference latency and computational costs in complex reasoning tasks.