Hierarchical Residual Policy Optimization for Generative Recommendations
Published in KDD 2026 Research Track, 2026
A post-training framework that converts item-level feedback into dense token-aligned credits for semantic-ID generative recommenders.
Recommended citation: Kaifeng Guo, Yiming Yang, Jingtong Gao, Guolei Zeng, Fukang Yang, Yukang Liang, Peng Jiang, Qingpeng Cai, and Xiangyu Zhao. (2026). "Hierarchical Residual Policy Optimization for Generative Recommendations." KDD 2026 Research Track.
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