Curriculum Guided Reinforcement Learning for Efficient Multi Hop Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2505.17391v1
• Date: Fri, 23 May 2025 02:01:15 GMT
• Title: Curriculum Guided Reinforcement Learning for Efficient Multi Hop Retrieval Augmented Generation
• Authors: Yuelyu Ji, Rui Meng, Zhuochun Li, Daqing He,
• Abstract summary: EVO-RAG is a curriculum-guided reinforcement learning framework.<n>It evolves a query-rewriting agent from broad early-stage exploration to concise late-stage refinement.<n>It boosts Exact Match by up to 4.6 points over strong RAG baselines while trimming average retrieval depth by 15 %.
• Score: 11.756344944226495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-augmented generation (RAG) grounds large language models (LLMs) in up-to-date external evidence, yet existing multi-hop RAG pipelines still issue redundant subqueries, explore too shallowly, or wander through overly long search chains. We introduce EVO-RAG, a curriculum-guided reinforcement learning framework that evolves a query-rewriting agent from broad early-stage exploration to concise late-stage refinement. EVO-RAG couples a seven-factor, step-level reward vector (covering relevance, redundancy, efficiency, and answer correctness) with a time-varying scheduler that reweights these signals as the episode unfolds. The agent is trained with Direct Preference Optimization over a multi-head reward model, enabling it to learn when to search, backtrack, answer, or refuse. Across four multi-hop QA benchmarks (HotpotQA, 2WikiMultiHopQA, MuSiQue, and Bamboogle), EVO-RAG boosts Exact Match by up to 4.6 points over strong RAG baselines while trimming average retrieval depth by 15 %. Ablation studies confirm the complementary roles of curriculum staging and dynamic reward scheduling. EVO-RAG thus offers a general recipe for building reliable, cost-effective multi-hop RAG systems.

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