Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints

Zhenyun Yin; Shujie Wang; Xuhong Wang; Xingjun Ma; Yinchun Wang

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arXiv · 2507.16727

Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints

Zhenyun Yin·Shujie Wang·Xuhong Wang·Xingjun Ma·Yinchun Wang

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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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Zhenyun Yin, Shujie Wang, Xuhong Wang, Xingjun Ma, Yinchun Wang. 2025-07-22. Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints. https://arxiv.org/abs/2507.16727

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