RL-Index: Reinforcement Learning for Retrieval Index Reasoning

A novel indexing system known as RL-Index has been introduced to enhance the retrieval of external knowledge in practical applications where queries are associated with relevant information through implicit reasoning, such as shared theorems or programming logic. Detailed in arXiv paper 2606.16316, this framework transitions reasoning from the query phase to the indexing phase by enriching documents with LLM-generated rationales that clearly represent hidden query-knowledge connections. This strategy seeks to minimize online latency and improve the use of reasoning semantics within the knowledge base. To refine the quality of these rationales, the technique utilizes Group Relative Policy Optimization (GRPO) and leverages retrieval similarity as a proxy reward, facilitating direct optimization of indexing choices for better retrieval performance. The paper was announced as a replace-cross on arXiv.