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 Knowledgeto-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. Code is available at https://github. com/SeedScientist/K2V . Recent large language models (LLMs), such as OpenAI-o1 (OpenAI et al., 2024), DeepSeek-R1 (Guo et al., 2025a), and Qwen3 (Yang et al., 2025a), have demonstrated remarkable progress in reasoning. Central to this progress is reinforcement learning with verifiable rewards (RLVR) (Shen et al., 2025; Peng et al., 2025; Stojanovski et al., 2025) , which drives the model to self-explore during training by comparing its outputs against a verifiable ground truth, thereby enhancing its capacity for complex problem-solving.