Paper Detail
Yongjia Lei, Nedim Lipka, Zhisheng Qi, Utkarsh Sahu, Koustava Goswami, Franck Dernoncourt, Ryan A. Rossi, Yu Wang
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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@misc{lei2026rl,
title = {RL-Index: Reinforcement Learning for Retrieval Index Reasoning},
author = {Yongjia Lei and Nedim Lipka and Zhisheng Qi and Utkarsh Sahu and Koustava Goswami and Franck Dernoncourt and Ryan A. Rossi and Yu Wang},
year = {2026},
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 o},
url = {https://huggingface.co/papers/2606.16316},
keywords = {reinforcement learning, Group Relative Policy Optimization, retrieval index reasoning, LLM-generated rationales, query-side reasoning, index-side reasoning, retrieval similarity, BRIGHT benchmark, downstream question-answering performance, online inference latency, huggingface daily},
eprint = {2606.16316},
archiveprefix = {arXiv},
}
{}