KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models
This addresses limitations like retrieval drift and strategy rigidity in RAG for complex reasoning, though it appears incremental as it builds on existing RAG methods with new mechanisms.
The paper tackled the problem of enhancing reasoning capabilities of large language models in complex multi-hop question-answering tasks by introducing KunLunBaizeRAG, a reinforcement learning-driven framework, resulting in significant improvements in exact match and LLM-judged scores across four benchmarks.
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.