Scent of Knowledge: Optimizing Search-Enhanced Reasoning with Information Foraging

• URL: http://arxiv.org/abs/2505.09316v1
• Date: Wed, 14 May 2025 12:13:38 GMT
• Title: Scent of Knowledge: Optimizing Search-Enhanced Reasoning with Information Foraging
• Authors: Hongjin Qian, Zheng Liu,
• Abstract summary: InForage is a reinforcement learning framework that formalizes retrieval-augmented reasoning as a dynamic information-seeking process.<n>We construct a human-guided dataset capturing iterative search and reasoning trajectories for complex, real-world web tasks.<n>These results highlight InForage's effectiveness in building robust, adaptive, and efficient reasoning agents.
• Score: 7.047640531842663
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Augmenting large language models (LLMs) with external retrieval has become a standard method to address their inherent knowledge cutoff limitations. However, traditional retrieval-augmented generation methods employ static, pre-inference retrieval strategies, making them inadequate for complex tasks involving ambiguous, multi-step, or evolving information needs. Recent advances in test-time scaling techniques have demonstrated significant potential in enabling LLMs to dynamically interact with external tools, motivating the shift toward adaptive inference-time retrieval. Inspired by Information Foraging Theory (IFT), we propose InForage, a reinforcement learning framework that formalizes retrieval-augmented reasoning as a dynamic information-seeking process. Unlike existing approaches, InForage explicitly rewards intermediate retrieval quality, encouraging LLMs to iteratively gather and integrate information through adaptive search behaviors. To facilitate training, we construct a human-guided dataset capturing iterative search and reasoning trajectories for complex, real-world web tasks. Extensive evaluations across general question answering, multi-hop reasoning tasks, and a newly developed real-time web QA dataset demonstrate InForage's superior performance over baseline methods. These results highlight InForage's effectiveness in building robust, adaptive, and efficient reasoning agents.

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