作者位次 #2/8ACL 2026Poster· Natural Language Processing
UR ^2 : Unify RAG and Reasoning through Reinforcement Learning
Weitao Li · Boran Xiang · Xiaolong Wang · Jingyi Ren · Ante Wang · Zhinan Gou · Weizhi Ma · Yang Liu
摘要
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...