multi-step-retrieval

Q-RAG introduces a reinforcement learning-based fine-tuning approach for embedder models to enable efficient multi-step retrieval, achieving state-of-the-art results on long-context benchmarks up to 10M tokens. This method provides a resource-efficient alternative to fine-tuning small LLMs for complex multi-step search tasks.