KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models

TL;DR

KunLunBaizeRAG is a reinforcement learning framework that significantly improves reasoning performance of large language models in complex multi-hop question-answering tasks by addressing retrieval and strategy limitations.

Contribution

The paper introduces novel mechanisms and training strategies to enhance reasoning in LLMs, surpassing traditional RAG limitations.

Findings

Improved exact match scores across four benchmarks

Enhanced reasoning accuracy in complex multi-hop questions

Robustness demonstrated in diverse reasoning scenarios

Abstract

This paper introduces KunLunBaizeRAG, a reinforcement learning-driven reasoning framework designed to enhance the reasoning capabilities of large language models (LLMs) in complex multi-hop question-answering tasks. The framework addresses key limitations of traditional RAG, such as retrieval drift, information redundancy, and strategy rigidity. Key innovations include the RAG-driven Reasoning Alignment (RDRA) mechanism, the Search-Think Iterative Enhancement (STIE) mechanism, the Network-Local Intelligent Routing (NLR) mechanism, and a progressive hybrid training strategy. Experimental results demonstrate significant improvements in exact match (EM) and LLM-judged score (LJ) across four benchmarks, highlighting the framework's robustness and effectiveness in complex reasoning scenarios.