@inproceedings{zhao-etal-2026-r,
title = "{R}-Search: Empowering {LLM} Reasoning with Search via Multi-Reward Reinforcement Learning",
author = "Zhao, Qingfei and
Wang, Ruobing and
Xu, Dingling and
Zha, Daren and
Bowen, Ma and
Wang, Zhichun and
Jia, Shijie and
Liu, Limin and
Wang, Xin",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1896/",
pages = "38030--38046",
ISBN = "979-8-89176-395-1",
abstract = "Large language models (LLMs) have notably progressed in multi-step and long-chain reasoning. However, extending their reasoning capabilities to encompass deep interactions with search remains a non-trivial challenge, as models often fail to identify optimal reasoning{--}search interaction trajectories, resulting in suboptimal responses. We propose R-Search, a novel reinforcement learning framework for Reasoning{--}Search integration, designed to enable LLMs to autonomously execute multi-step reasoning with deep search interaction, and learn optimal reasoning{--}search interaction trajectories via multi-reward signals, improving response quality in complex logic- and knowledge-intensive tasks. R-Search guides the LLM to dynamically decide when to search or reason, while globally integrating key evidence to enhance deep knowledge interaction between reasoning and search. During RL training, R-Search provides multi-type rewards to jointly optimize the reasoning{--}search trajectory. Experiments on seven datasets show that R-Search significantly outperforms mainstream RAG baselines."
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<abstract>Large language models (LLMs) have notably progressed in multi-step and long-chain reasoning. However, extending their reasoning capabilities to encompass deep interactions with search remains a non-trivial challenge, as models often fail to identify optimal reasoning–search interaction trajectories, resulting in suboptimal responses. We propose R-Search, a novel reinforcement learning framework for Reasoning–Search integration, designed to enable LLMs to autonomously execute multi-step reasoning with deep search interaction, and learn optimal reasoning–search interaction trajectories via multi-reward signals, improving response quality in complex logic- and knowledge-intensive tasks. R-Search guides the LLM to dynamically decide when to search or reason, while globally integrating key evidence to enhance deep knowledge interaction between reasoning and search. During RL training, R-Search provides multi-type rewards to jointly optimize the reasoning–search trajectory. Experiments on seven datasets show that R-Search significantly outperforms mainstream RAG baselines.</abstract>
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%0 Conference Proceedings
%T R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning
%A Zhao, Qingfei
%A Wang, Ruobing
%A Xu, Dingling
%A Zha, Daren
%A Bowen, Ma
%A Wang, Zhichun
%A Jia, Shijie
%A Liu, Limin
%A Wang, Xin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhao-etal-2026-r
%X Large language models (LLMs) have notably progressed in multi-step and long-chain reasoning. However, extending their reasoning capabilities to encompass deep interactions with search remains a non-trivial challenge, as models often fail to identify optimal reasoning–search interaction trajectories, resulting in suboptimal responses. We propose R-Search, a novel reinforcement learning framework for Reasoning–Search integration, designed to enable LLMs to autonomously execute multi-step reasoning with deep search interaction, and learn optimal reasoning–search interaction trajectories via multi-reward signals, improving response quality in complex logic- and knowledge-intensive tasks. R-Search guides the LLM to dynamically decide when to search or reason, while globally integrating key evidence to enhance deep knowledge interaction between reasoning and search. During RL training, R-Search provides multi-type rewards to jointly optimize the reasoning–search trajectory. Experiments on seven datasets show that R-Search significantly outperforms mainstream RAG baselines.
%U https://aclanthology.org/2026.findings-acl.1896/
%P 38030-38046
Markdown (Informal)
[R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning](https://aclanthology.org/2026.findings-acl.1896/) (Zhao et al., Findings 2026)
ACL
- Qingfei Zhao, Ruobing Wang, Dingling Xu, Daren Zha, Ma Bowen, Zhichun Wang, Shijie Jia, Limin Liu, and Xin Wang. 2026. R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 38030–38046, San Diego, California, United States. Association for Computational Linguistics.