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cs.IR2026
GRP v0.1 Technical Report
Wenfeng Zhuo, Vincent Xue, Charles Wei +19
Industrial recommendation systems rely on multi-stage cascades whose retrieval, ranking, and serving components are difficult to replace jointly. We present GRP, a generative recom…
cs.IR2026
A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems
Yichu Zhou, Mehdi Ben Ayed, Lin Yang +9
Large-scale recommenders encode multi-objective trade-offs by combining multiple predicted outcomes into a single utility score. Although this utility layer can be updated independ…