ReSearch: Learning to Reason with Search for LLMs via Reinforcement
  Learning
Authors: name: Mingyang Chen, name: Tianpeng Li, name: Haoze Sun, name: Yijie Zhou, name: Chenzheng Zhu, name: Fan Yang, name: Zenan Zhou, name: Weipeng Chen, name: Haofen Wang, name: Jeff Z. Pan, name: Wen Zhang, name: Huajun Chen

Large Language Models (LLMs) have shown remarkable capabilities in reasoning,
exemplified by the success of OpenAI-o1 and DeepSeek-R1. However, integrating
reasoning with external search processes remains challenging, especially for
complex multi-hop questions requiring multiple retrieval steps. We propose
ReSearch, a novel framework that trains LLMs to Reason with Search via
reinforcement learning without using any supervised data on reasoning steps.
Our approach treats search operations as integral components of the reasoning
chain, where when and how to perform searches is guided by text-based thinking,
and search results subsequently influence further reasoning. We train ReSearch
on Qwen2.5-7B(-Instruct) and Qwen2.5-32B(-Instruct) models and conduct
extensive experiments. Despite being trained on only one dataset, our models
demonstrate strong generalizability across various benchmarks. Analysis reveals
that ReSearch naturally elicits advanced reasoning capabilities such as
reflection and self-correction during the reinforcement learning process.