OneLive: Dynamically Unified Generative Framework for Live-Streaming Recommendation

Shen Wang; Yusheng Huang; Ruochen Yang; Shuang Wen; Pengbo Xu; Jiangxia Cao; Yueyang Liu; Kuo Cai; Chengcheng Guo; Shiyao Wang; Xinchen Luo; Qiang Luo; Ruiming Tang; Shuang Yang; Zhaojie Liu; Guorui Zhou; Han Li; Kun Gai

arXiv · 2602.08612

OneLive: Dynamically Unified Generative Framework for Live-Streaming Recommendation

Shen Wang·Yusheng Huang·Ruochen Yang·Shuang Wen·Pengbo Xu·Jiangxia Cao·Yueyang Liu·Kuo Cai·Chengcheng Guo·Shiyao Wang·Xinchen Luo·Qiang Luo·Ruiming Tang·Shuang Yang·Zhaojie Liu·Guorui Zhou·Han Li·Kun Gai

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Abstract

Live-streaming recommender system serves as critical infrastructure that bridges the patterns of real-time interactions between users and authors. Similar to traditional industrial recommender systems, live-streaming recommendation also relies on cascade architectures to support large-scale concurrency. Recent advances in generative recommendation unify the multi-stage recommendation process with Transformer-based architectures, offering improved scalability and higher computational efficiency. However, the inherent complexity of live-streaming prevents the direct transfer of these methods to live-streaming scenario, where continuously evolving content, limited lifecycles, strict real-time constraints, and heterogeneous multi-objectives introduce unique challenges that invalidate static tokenization and conventional model framework. To address these issues, we propose OneLive, a dynamically unified generative recommendation framework tailored for live-streaming scenario. OneLive integrates four key components: (i) A Dynamic Tokenizer that continuously encodes evolving real-time live content fused with behavior signal through residual quantization; (ii) A Time-Aware Gated Attention mechanism that explicitly models temporal dynamics for timely decision making; (iii) An efficient decoder-only generative architecture enhanced with Sequential MTP and QK Norm for stable training and accelerated inference; (iv) A Unified Multi-Objective Alignment Framework reinforces policy optimization for personalized preferences.

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Shen Wang, Yusheng Huang, Ruochen Yang, Shuang Wen, Pengbo Xu, Jiangxia Cao, Yueyang Liu, Kuo Cai, Chengcheng Guo, Shiyao Wang, Xinchen Luo, Qiang Luo, Ruiming Tang, Shuang Yang, Zhaojie Liu, Guorui Zhou, Han Li, Kun Gai. 2026-02-09. OneLive: Dynamically Unified Generative Framework for Live-Streaming Recommendation. https://arxiv.org/abs/2602.08612

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