Author

Changsung Kang

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#natural language process... Preprint Sep 2026

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

CoGR is introduced, a retrieval framework that instead trains LLMs to directly construct retrieval representations on both query and item sides, and shows stable co-evolution and increasingly aligned query--item keyword spaces over training.

Runpeng Dai, Kai-Li Huang, Chang-Sung Kang et al. · 2 citations

Book Open access Jul 2026

Learning to Summarize for Search Relevance with Reinforcement Learning

ReLSum is a reinforcement learning framework that generates concise, relevance-optimized product summaries for search ranking by using downstream relevance scores as reward signals and enables summaries to be generated and cached offline, ensuring no additional inference latency at serving time.

Nitin Yadav, Changsung Kang, Hongwei Shang · Annual International ACM SIG... · 0 citations