RL-Index: Reinforcement Learning for Retrieval Index Reasoning

• URL: http://arxiv.org/abs/2606.16316v1
• Date: Mon, 15 Jun 2026 07:22:36 GMT
• Title: RL-Index: Reinforcement Learning for Retrieval Index Reasoning
• Abstract summary: We propose RL-Index, an agentic indexing framework that formulates retrieval index reasoning as a reinforcement learning problem.<n> RL-Index shifts reasoning to the indexing stage by augmenting documents with rationales that explicitly encode the latent query-knowledge relationship.<n>Experiments on the BRIGHT benchmark demonstrate that RL-Index consistently improves both retrieval and downstream question-answering performance.
• Score: 73.82948316789812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieving external knowledge is essential for solving real-world tasks, yet it remains challenging when the relationship between a query and its relevant knowledge involves implicit and complex reasoning beyond surface-level semantic or lexical matching (e.g., mathematical problems relying on the same theorem or coding requiring deep reasoning). Existing approaches primarily rely on query-side reasoning (e.g., query rewriting), which introduces significant online latency and underutilizes the opportunity to perform reasoning over the knowledge corpus itself (i.e., index-side reasoning). In this paper, we propose RL-Index, an agentic indexing framework that formulates retrieval index reasoning as a reinforcement learning problem. Instead of performing reasoning at query time, RL-Index shifts reasoning to the indexing stage by augmenting documents with LLM-generated rationales that explicitly encode the latent query-knowledge relationship. To optimize the quality of these rationales, we employ Group Relative Policy Optimization (GRPO) and use retrieval similarity as a verifiable reward signal, enabling direct optimization of indexing decisions for retrieval effectiveness. Extensive experiments on the BRIGHT benchmark demonstrate that RL-Index consistently improves both retrieval and downstream question-answering performance, while significantly reducing online inference latency. Moreover, the learned rationale augmentation generalizes across diverse retrievers and generators, highlighting its robustness as a plug-and-play indexing strategy across different retrieval systems.

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