AI RESEARCH
Q-RAG: Long Context Multi-step Retrieval via Value-based Embedder Training
arXiv CS.LG
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ArXi:2511.07328v2 Announce Type: replace Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and inference cost. However, most existing RAG methods focus on single-step retrieval, which is often insufficient for answering complex questions that require multi-step search. Recently, multi-step retrieval approaches have emerged, typically involving the fine-tuning of small LLMs to perform multi-step retrieval.