Efficient, Property-Aligned Fan-Out Retrieval via RL-Compiled Diffusion

• URL: http://arxiv.org/abs/2603.06397v1
• Date: Fri, 06 Mar 2026 15:42:33 GMT
• Title: Efficient, Property-Aligned Fan-Out Retrieval via RL-Compiled Diffusion
• Abstract summary: Fan-out retrieval is often employed to generate diverse subqueries to retrieve item sets.<n>We propose R4T (Retrieve-for-Train), which uses RL once as an objective transducer in a three-step process.<n>We show that R4T improves retrieval quality relative to strong baselines while reducing query-time fan-out latency by an order of magnitude.
• Score: 32.795057895718365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many modern retrieval problems are set-valued: given a broad intent, the system must return a collection of results that optimizes higher-order properties (e.g., diversity, coverage, complementarity, coherence) while remaining grounded with respect to a fixed database. Set-valued objectives are typically non-decomposable and are not captured by existing supervised (query, content) datasets which only prioritize top-1 retrieval. Consequently, fan-out retrieval is often employed to generate diverse subqueries to retrieve item sets. While reinforcement learning (RL) can optimize set-level objectives via interaction, deploying an RL-tuned LLM for fan-out retrieval is prohibitively expensive at inference time. Conversely, diffusion-based generative retrieval enables efficient single-pass fan-out in embedding space, but requires objective-aligned training targets. To address these issues, we propose R4T (Retrieve-for-Train), which uses RL once as an objective transducer in a three-step process: (i) train a fan-out LLM with composite set-level rewards, (ii) synthesize objective-consistent training pairs, and (iii) train a lightweight diffusion retriever to model the conditional distribution of set-valued outputs. Across large-scale fashion and music benchmarks consisting of curated item sets, we show that R4T improves retrieval quality relative to strong baselines while reducing query-time fan-out latency by an order of magnitude.

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