  Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by
retrieving relevant memories from an external database. However, existing RAG
methods typically organize all memories in a whole database, potentially
limiting focus on crucial memories and introducing noise. In this paper, we
introduce a multiple partition paradigm for RAG (called M-RAG), where each
database partition serves as a basic unit for RAG execution. Based on this
paradigm, we propose a novel framework that leverages LLMs with Multi-Agent
Reinforcement Learning to optimize different language generation tasks
explicitly. Through comprehensive experiments conducted on seven datasets,
spanning three language generation tasks and involving three distinct language
model architectures, we confirm that M-RAG consistently outperforms various
baseline methods, achieving improvements of 11%, 8%, and 12% for text
summarization, machine translation, and dialogue generation, respectively.
