Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints

• URL: http://arxiv.org/abs/2507.16727v2
• Date: Wed, 23 Jul 2025 03:52:14 GMT
• Title: Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints
• Authors: Zhenyun Yin, Shujie Wang, Xuhong Wang, Xingjun Ma, Yinchun Wang,
• Abstract summary: We propose textbfDeliberative Searcher, the first framework to integrate certainty calibration with retrieval-based search for open-domain question answering.<n>The agent performs multi-step reflection and verification over Wikipedia data and is trained with a reinforcement learning algorithm that optimize for accuracy under a soft reliability constraint.
• Score: 18.10515528600634
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Improving the reliability of large language models (LLMs) is critical for deploying them in real-world scenarios. In this paper, we propose \textbf{Deliberative Searcher}, the first framework to integrate certainty calibration with retrieval-based search for open-domain question answering. The agent performs multi-step reflection and verification over Wikipedia data and is trained with a reinforcement learning algorithm that optimizes for accuracy under a soft reliability constraint. Empirical results show that proposed method improves alignment between model confidence and correctness, leading to more trustworthy outputs. This paper will be continuously updated.

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