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cs.IR2026
Compress, Cross and Scale: Multi-Level Compression Cross Networks for Efficient Scaling in Recommender Systems
Heng Yu, Xiangjun Zhou, Jie Xia +4
Modeling high-order feature interactions efficiently is a central challenge in click-through rate and conversion rate prediction. Modern industrial recommender systems are predomin…
cs.LG2026
Unifying Stable Optimization and Reference Regularization in RLHF
Li He, Qiang Qu, He Zhao +4
Reinforcement Learning from Human Feedback (RLHF) has advanced alignment capabilities significantly but remains hindered by two core challenges: \textbf{reward hacking} and \textbf…