@inproceedings{qi-etal-2026-language,
    title = "Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation",
    author = "Qi, Rui  and
      Mo, Fengran  and
      Chen, Yufeng  and
      Zhang, Xue  and
      Wang, Shuo  and
      Li, Hongliang  and
      Jinan, Xu  and
      Jiang, Meng  and
      Nie, Jian-Yun  and
      Huang, Kaiyu",
    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.700/",
    pages = "14291--14310",
    ISBN = "979-8-89176-395-1",
    abstract = "Multilingual retrieval-augmented generation (MRAG) requires models to effectively acquire and integrate beneficial external knowledge from multilingual collections. However, most existing studies employ a unitive process where queries of equivalent semantics across different languages are processed through a single-turn retrieval and subsequent optimization. Such a ``one-size-fits-all'' strategy is often suboptimal in multilingual settings, as the models occur to knowledge bias and conflict during the interaction with the search engine. To alleviate the issues, we propose LcRL, a multilingual search-augmented reinforcement learning framework that integrates a language-coupled Group Relative Policy Optimization into the policy and reward models. We adopt the language-coupled group sampling in the rollout module to reduce knowledge bias, and regularize an auxiliary anti-consistency penalty in the reward models to mitigate the knowledge conflict. Experimental results demonstrate that {~}not only achieves competitive performance but is also appropriate for various practical scenarios such as constrained training data and retrieval over collections encompassing a large number of languages. Our code is available at https://anonymous.4open.science/r/LcRL-B4EF."
}