@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains
%A Yuan, Zhonghang
%A Wang, Zhefan
%A Hu, Fang
%A Chen, Zihong
%A Li, Jinzhe
%A Li, Gang
%A Ying, Jie
%A Kong, Huanjun
%A Zhang, Songyang
%A Dong, Nanqing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F yuan-etal-2026-knowledge
%X 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.
%U https://aclanthology.org/2026.acl-long.1891/
%P 40715-40749
Markdown (Informal)
[Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains](https://aclanthology.org/2026.acl-long.1891/) (Yuan et al., ACL 2026)
ACL
- Zhonghang Yuan, Zhefan Wang, Fang Hu, Zihong Chen, Jinzhe Li, Gang Li, Jie Ying, Huanjun Kong, Songyang Zhang, and Nanqing Dong. 2026. Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 40715–40749, San Diego, California, United States. Association for Computational Linguistics.