Research

Document Optimization for Black-Box Retrieval via Reinforcement Learning

Researchers use reinforcement learning to optimize document content for black-box retrieval systems, solving the challenge of adapting documents when the retrieval model's internals are inaccessible.

Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.CL (Computation & Language)BY sys://pipeline

Research paper proposing reinforcement learning as a method to optimize documents for black-box retrieval systems—systems without gradient access. Addresses the challenge of adapting document content when the retrieval model is opaque.

Read original at arXiv CS.CL (Computation & Language)

Align then Train: Efficient Retrieval Adapter Learning

Two-stage adapter training approach (align then train) reduces computational costs for retrieval-augmented systems through pre-alignment optimization.