@inproceedings{zhao-etal-2026-r-search,
    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://preview.aclanthology.org/ingest-acl/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."
}