SAAS: Self-Aware Reinforcement Learning for Over-Search Mitigation in Agentic Search
Summary
SAAS introduces a reinforcement learning framework that enhances agent self-awareness to reduce unnecessary searches in LLM-based question answering systems, balancing accuracy and computational cost.
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Paper page - SAAS: Self-Aware Reinforcement Learning for Over-Search Mitigation in Agentic Search
Source: https://huggingface.co/papers/2605.29796
Abstract
SAAS introduces a reinforcement learning framework that enhances agent self-awareness to reduce unnecessary searches in LLM-based question answering systems.
Agentic searchenablesLLMsto solve complexmulti-hop questionsthroughiterative reasoningandexternal search. Despite the effectiveness, these systems often suffer from a critical limitation in practice: agents fail to recognize their own knowledge boundaries, blindly triggering searches when internal knowledge suffices and failing to terminate search even when adequate evidence has been collected. The lack ofself-awarenessleads to severeover-search, incurring substantial inference latency and prohibitive computational cost. To this end, we propose SAAS, a novelRL frameworkdesigned to cultivate dynamicself-awarenessthat precisely regulates search behavior without compromising accuracy. SAAS introduces three key components: (i) asearch boundary modelingmechanism, which identifies the search boundary under the evolving policy by contrasting search-disabled and search-enabled rollouts; (ii) aboundary-aware reward module, which translates this boundary awareness intotrajectory-level penalties, suppressing unnecessary and redundant searches; and (iii) astage-wise optimizationstrategy, which leverages a sequential curriculum to prioritize reasoning over search regularization, thereby avoidingreward hacking. Extensive experiments demonstrate that SAAS substantially reducesover-search, while maintaining accuracy. Our code is anonymously released at https://github.com/XMUDeepLIT/SAAS.
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