RL-Index: Reinforcement Learning for Retrieval Index Reasoning

arXiv:2606.16316v2 Announce Type: replace-cross Abstract: Retrieving external knowledge is crucial for real-world tasks but remains difficult when queries and relevant knowledge are linked by implicit reasoning (e.g., shared theorems or coding logic). Existing methods rely mainly on query-side reasoning, leading to high online latency and underutilizing the reasoning semantics within the knowledge corpus. In this paper, we propose $\textbf{RL-Index}$, an 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 proxy 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.

Abstract:Retrieving external knowledge is crucial for real-world tasks but remains difficult when queries and relevant knowledge are linked by implicit reasoning (e.g., shared theorems or coding logic). Existing methods rely mainly on query-side reasoning, leading to high online latency and underutilizing the reasoning semantics within the knowledge corpus. In this paper, we propose $\textbf{RL-Index}$, an 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 proxy 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.

Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2606.16316 [cs.IR]

(or arXiv:2606.16316v2 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2606.16316 Focus to learn more arXiv-issued DOI via DataCite

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RL-Index: Reinforcement Learning for Retrieval Index Reasoning

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