RelayGR: Scaling Long-Sequence Generative Recommendation via Cross-Stage Relay-Race Inference

📝 Original Info

• Title: RelayGR: Scaling Long-Sequence Generative Recommendation via Cross-Stage Relay-Race Inference
• ArXiv ID: 2601.01712
• Date: 2026-01-05
• Authors: Jiarui Wang, Huichao Chai, Yuanhang Zhang, Zongjin Zhou, Wei Guo, Xingkun Yang, Qiang Tang, Bo Pan, Jiawei Zhu, Ke Cheng, Yuting Yan, Shulan Wang, Yingjie Zhu, Zhengfan Yuan, Jiaqi Huang, Yuhan Zhang, Xiaosong Sun, Zhinan Zhang, Hong Zhu, Yongsheng Zhang, Tiantian Dong, Zhong Xiao, Deliang Liu, Chengzhou Lu, Yuan Sun, Zhiyuan Chen, Xinming Han, Zaizhu Liu, Yaoyuan Wang, Ziyang Zhang, Yong Liu, Jinxin Xu, Yajing Sun, Zhoujun Yu, Wenting Zhou, Qidong Zhang, Zhengyong Zhang, Zhonghai Gu, Yibo Jin, Yongxiang Feng, Pengfei Zuo

📝 Abstract

Real-time recommender systems execute multi-stage cascades (retrieval, pre-processing, fine-grained ranking) under strict tail-latency SLOs, leaving only tens of milliseconds for ranking. Generative recommendation (GR) models can improve quality by consuming long user-behavior sequences, but in production their online sequence length is tightly capped by the ranking-stage P99 budget. We observe that the majority of GR tokens encode user behaviors that are independent of the item candidates, suggesting an opportunity to pre-infer a user-behavior prefix once and reuse it during ranking rather than recomputing it on the critical path. Realizing this idea at industrial scale is non-trivial: the prefix cache must survive across multiple pipeline ...

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