GraphRAG-Router Framework Uses Reinforcement Learning for Cost-Efficient AI Question Answering

A new framework called GraphRAG-Router addresses inefficiencies in existing GraphRAG systems for knowledge-intensive question answering. Traditional approaches rely on fixed retrieval frameworks and single, often expensive, large language models for all queries, regardless of complexity. This static design limits adaptability and incurs unnecessary computational costs. GraphRAG-Router introduces a hierarchical routing strategy to coordinate multiple, heterogeneous GraphRAGs and generator LLMs. The system is initially warmed up through supervised fine-tuning before undergoing optimization via a two-stage reinforcement learning procedure. The second stage of this procedure incorporates a curriculum cost-aware reward mechanism. The goal is to dynamically match query complexity with appropriate computational resources, improving both efficiency and performance. The research was announced on arXiv under the identifier 2604.16401v1, categorized as a cross-announcement. Graph-based retrieval-augmented generation has recently become a significant paradigm, particularly for tasks requiring structured evidence organization and multi-hop reasoning.