Efficient, Property-Aligned Fan-Out Retrieval via RL-Compiled Diffusion
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 a...
| Authors | Pengcheng Jiang, Judith Yue Li, Moonkyung Ryu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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