ReSearch: Learning to Reason with Search for LLMs via Reinforcement
  Learning
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.
67e365b0dcfc2aeae1bf3da2
Mingyang Chen
67e365b0dcfc2aeae1bf3da3
Tianpeng Li
67e365b0dcfc2aeae1bf3da4
Haoze Sun
67e365b0dcfc2aeae1bf3da5
Yijie Zhou
67e365b0dcfc2aeae1bf3da6
Chenzheng Zhu
67e365b0dcfc2aeae1bf3da7
Fan Yang
67e365b0dcfc2aeae1bf3da8
Zenan Zhou
67e365b0dcfc2aeae1bf3da9
Weipeng Chen
67e365b0dcfc2aeae1bf3daa
Haofen Wang
67e365b0dcfc2aeae1bf3dab
Jeff Z. Pan
67e365b0dcfc2aeae1bf3dac
Wen Zhang
67e365b0dcfc2aeae1bf3dad
Huajun Chen
ReSearch: Learning to Reason with Search for LLMs via Reinforcement
  Learning
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.
Large Language Models (LLMs) OpenAI-o1 DeepSeek-R1 complex multi-hop questions ReSearch reinforcement learning text-based thinking reflection self-correction Qwen2.5-7B(-Instruct) Qwen2.5-32B(-Instruct)