SOURCE-LINKED INTELLIGENCE

It Takes Two to Match: Co-Evolving Generative Retriever with Reinforcement Learning

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Retrieval is the first stage of modern search and advertising systems, selecting a candidate set from a large item universe for downstream ranking and auction. Recent work increasingly leverages LLMs to improve retrieval through query expansion, data synthesis, and retrieval-feedback training. However, the generative component is typically used for query-side augmentation, while final matching is still delegated to a downstream retriever. We introduce CoGR, a retrieval framework that instead trains LLMs to directly construct retrieval representations on both query and item sides. Each generato

Read original source ↗ Open in workspace

recordType

paper

region

Global