UR$^2$: Unify RAG and Reasoning through Reinforcement Learning 事件

UR$^2$: Unify RAG and Reasoning through Reinforcement Learning arXiv:2508.06165v5 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong capabilities through two complementary paradigms: Retrieval-Augmented Generation (RAG) for knowledge grounding and Reinforcement Learning from Verifiable Rewards (RLVR) for complex reasoning. However, existing attempts to unify these paradigms remain narrow in scope, typically limited to open-domain QA with fixed retrieval settings, wh