Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning (2501.15228v1)

Abstract: Retrieval-augmented generation (RAG) is extensively utilized to incorporate external, current knowledge into LLMs, thereby minimizing hallucinations. A standard RAG pipeline may comprise several components, such as query rewriting, document retrieval, document filtering, and answer generation. However, these components are typically optimized separately through supervised fine-tuning, which can lead to misalignments between the objectives of individual modules and the overarching aim of generating accurate answers in question-answering (QA) tasks. Although recent efforts have explored reinforcement learning (RL) to optimize specific RAG components, these approaches often focus on overly simplistic pipelines with only two components or do not adequately address the complex interdependencies and collaborative interactions among the modules. To overcome these challenges, we propose treating the RAG pipeline as a multi-agent cooperative task, with each component regarded as an RL agent. Specifically, we present MMOA-RAG, a Multi-Module joint Optimization Algorithm for RAG, which employs multi-agent reinforcement learning to harmonize all agents' goals towards a unified reward, such as the F1 score of the final answer. Experiments conducted on various QA datasets demonstrate that MMOA-RAG improves the overall pipeline performance and outperforms existing baselines. Furthermore, comprehensive ablation studies validate the contributions of individual components and the adaptability of MMOA-RAG across different RAG components and datasets. The code of MMOA-RAG is on https://github.com/chenyiqun/MMOA-RAG.

• The paper introduces MMOA-RAG, a novel framework that leverages multi-agent reinforcement learning to holistically align and optimize RAG systems.
• It demonstrates significant performance gains, improving metrics like F1 score, accuracy, and exact match on datasets such as HotpotQA.
• Detailed ablation studies confirm that multi-agent collaboration is crucial, as removing agents leads to notable performance degradation.

Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning

This paper introduces an innovative approach to optimizing Retrieval-Augmented Generation (RAG) through Multi-Agent Reinforcement Learning (MARL), a method that addresses the shortcomings of independently optimizing components in a typical RAG pipeline. In conventional RAG systems, components such as query rewriting, document retrieval, document filtering, and answer generation are optimized separately, often leading to objective misalignments. The proposed framework, MMOA-RAG, models these components as agents within a multi-agent cooperative task, each optimized to collaboratively improve the overall system performance through shared objectives.

The paper conceptualizes the RAG pipeline as a multi-module cooperative task, assigning each component the role of an RL agent. Specifically, the MMOA-RAG employs MARL to ensure that all agents are harmonized towards a unified reward—namely, the F1 score of the final generated answer. The key innovation here is the use of multi-agent reinforcement learning to align the disparate goals of individual components with the overarching aim of enhancing the quality of generated answers in question-answering tasks.

Significant findings highlight the efficacy of MMOA-RAG over existing baseline methods. Utilizing datasets such as HotpotQA, 2WikiMultihopQA, and AmbigQA, the paper demonstrates the superiority of MMOA-RAG in improving metrics such as the F1 score, accuracy, and exact match over other existing RL-based and supervised fine-tuning (SFT) approaches. Notably, MMOA-RAG consistently outperformed methods like SELF-RAG and Rewrite-Retrieve-Read, primarily due to its holistic optimization strategy.

A noteworthy aspect of the research is its comprehensive ablation studies, which underscore the importance of multi-agent collaboration. The results from these studies show that removing any of the agents (e.g., the Query Rewriter or Selector) from the optimization process degrades performance, thereby highlighting the value of joint optimization. The robustness of MMOA-RAG across various pipeline configurations further validates its flexibility and adaptability in different RAG systems.

The implications of this research are substantial, both from theoretical and practical perspectives. Theoretically, the paper advances the understanding of MARL applications in NLP tasks, providing a novel way to view pipeline optimizations for LLMs. Practically, the collaborative optimization framework can be extended to other modular systems, offering potential improvements in domains requiring seamless integration of multiple components.

Future developments in AI can build upon this research by exploring other multi-agent architectures or integrating additional optimization strategies. For instance, the extension of the MMOA-RAG framework to incorporate dynamic pipeline reconfiguration based on task-specific requirements may yield further gains in efficiency and effectiveness. Additionally, integrating this approach in real-world applications like knowledge management systems, virtual assistants, and enhanced search engines could substantially mitigate challenges such as outdated information and hallucinations in AI-generated content.