@inproceedings{yuan-etal-2026-knowledge,
    title = "Knowledge-to-Verification: Exploring {RLVR} for {LLM}s in Knowledge-Intensive Domains",
    author = "Yuan, Zhonghang  and
      Wang, Zhefan  and
      Hu, Fang  and
      Chen, Zihong  and
      Li, Jinzhe  and
      Li, Gang  and
      Ying, Jie  and
      Kong, Huanjun  and
      Zhang, Songyang  and
      Dong, Nanqing",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1891/",
    pages = "40715--40749",
    ISBN = "979-8-89176-390-6",
    abstract = "Reinforcement learning with verifiable rewards (RLVR) has demonstrated promising potential to enhance the reasoning capabilities of large language models (LLMs) in domains such as mathematics and coding. However, its applications on knowledge-intensive domains have not been effectively explored due to the scarcity of high-quality verifiable data. Furthermore, current RLVR focuses solely on the correctness of final answers, leading to the limitations of flawed reasoning and sparse reward signals. In this work, we propose Knowledge-to-Verification (K2V), a framework that extends RLVR to knowledge-intensive domains through automated verifiable data synthesis, while enabling verification of the LLM{'}s reasoning process. Extensive experiments demonstrate that K2V enhances the reasoning of LLM in knowledge-intensive domains without significantly compromising the model{'}s general capabilities. This study also suggests that integrating automated data synthesis with reasoning verification is a promising direction to enhance model capabilities in these broader domains."
}