Document Optimization for Black-Box Retrieval via Reinforcement Learning
Generative large language models (LLMs) are increasingly used as inference-time components in retrieval pipelines, for tasks such as query rewriting and document reranking. However, these online approaches place costly autoregressive computation directly on the latency-critical retrieval path. We explore an alternative axis: using LLMs to improve documents instead, rewriting them into better representations and shifting computation offline. Yet producing a useful document rewrite is not straightforward: retrieval is inherently discriminative, so an effective rewrite must make a document more similar to relevant queries than competing candidates under the retriever's notion of similarity. ...
| Authors | Omri Uzan, Ron Polonsky, Douwe Kiela et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 April 2026 |
| Last updated at source | 7 October 2026 |
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