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

62 upvotes · 1 SEP 2026 · Runpeng Dai, Kaili Huang, Changsung Kang et al.

This paper proposes a new retrieval framework called CoGR, which uses two generative models to co-evolve and optimize retrieval representations on both the query and item sides, leading to improved search and advertising performance. Practitioners might care because CoGR can potentially lead to better retrieval results and more efficient search systems.

generative retrieverreinforcement learningcontinual learninginverted indexkeyword-based retrievalLLMs