LI-RAG: Latent Iterative RAG for Complex QA

Retrieval-augmented generation (RAG) grounds large language models (LLMs) in retrieved passages, but for multi-hop questions a single round of retrieval often misses the later evidence, which cannot be located from the question alone. Existing multi-round methods either call an LLM at every retrieval step, so that latency grows with the number of hops, or train the retriever on annotated evidence chains, which are expensive to collect. We propose LI-RAG (Latent Iterative RAG), which trains a multi-round query encoder on the decomposition reasoning paths of an LLM, supervising each round's query embedding with the sub-question the LLM asks at that step and the evidence it selects. At inference, retrieval takes only a few encoder forward passes and no LLM call. Trained on 4,000 questions, LI-RAG outperforms multi-round retrievers trained on the gold evidence of the same questions on four multi-hop benchmarks, performs on par with or better than a retriever trained on 46 times as much annotated data, and remains competitive with a method that calls an LLM at every retrieval step.