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Learning to Program Retrievals: Composing Search Primitives via Reinforcement Learning

Kyuyoung Kim, Zilin Xiao, Zhiyuan Wang, Avinash Atreya, Ke Yang, Jinwoo Shin · Oct 17, 2026

Citations: 0

Match reason: Matched by broad semantic/index fallback.

Score: 45% Moderate protocol signal Freshness: Hot Status: Fallback
Automatic Metrics Long Horizon General
  • RPL consistently improves open-domain QA performance across model sizes and in- and out-of-domain benchmarks, with particularly large gains on multi-hop tasks.
  • Our results show that programmable search expands the retrieval strategies that agents can learn, while program mutation provides an effective mechanism for exploring useful program structures beyond direct on-policy sampling.
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