Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains
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 t...
| Authors | Zhonghang Yuan, Zhefan Wang, Fang Hu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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