Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints

Improving the reliability of large language models (LLMs) is critical for deploying them in real-world scenarios. In this paper, we propose $\mathbf{\text{Deliberative}}\mathbf{\text{Searcher}}$, 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.