Improving RAG through Multi-Agent RL

This work treats RAG as a multi-agent cooperative task to improve answer generation quality. It models RAG components like query rewriting, document selection, and answer generation as reinforcement learning agents working together toward generating accurate answers. It applies Multi-Agent Proximal Policy Optimization (MAPPO) to jointly optimize all agents with a shared reward based on answer quality. Besides improvements on popular benchmarks, the framework shows strong generalization capabilities in out-of-domain scenarios and maintains effectiveness across different RAG system configurations.