UniPolicy is a unified objective‑specific policy framework for search advertising that jointly optimizes relevance, click propensity, and commercial value. It uses objective‑aware prefix tokens, sparse MoE‑LoRA routing, and residual FFNs to decouple parameters within a shared backbone, and constructs pairwise preferences from multi‑stage behavioral feedback to strengthen clicked candidates. In large‑scale offline tests and a 7‑day online A/B test, UniPolicy improves CTR by 0.71%, RPS by 1.58%, and advertising revenue by 1.32% while keeping serving latency stable.
By Kun Yao, Yuhang Zhou, Yichi Zhang, Zeliang Tong, Shengri Xue, Haitao Wang, Siyu Lu, Qianlong Xie, Xingxing Wang
arXiv:2512.23067v3 Announce Type: replace
Abstract: Personalized alignment from preference data has focused primarily on improving personal reward model (RM) accuracy, with the implicit assumption th...
By Fady Rezk, Yuangang Pan, Chuan-Sheng Foo, Xun Xu, Nancy Chen, Henry Gouk, Timothy Hospedales
arXiv:2609.08211v1 Announce Type: new
Abstract: Real-world Multi-Objective Reinforcement Learning (MORL) often suffers from sparse rewards, reward conflicts, and late-stage reward tug-of-war, causing...
By Shanwen Mao, Hao Zhang, Guangtao nie, Zhiheng Li, Huimu Wang, Sulong Xu, Gu Simiu
The paper introduces GR4AD‑X, an enhanced generative recommendation framework for large‑scale advertising that tackles three key limitations of its predecessor: unreliable deterministic SID assignment, limited scalability of backbone models, and weak credit assignment in sequence‑level value optimization. GR4AD‑X improves SID reliability via Multi‑Path assignment, expands model capacity across complementary dimensions for better efficiency, and aligns generation with commercial value through Unified Value‑Guided Generation and Discrimination (UniVGD), which optimizes prefix‑level rewards, prunes beams, and reranks discriminatively. Online A/B tests on Kuaishou’s advertising platform show a 3.07% revenue increase over the baseline while meeting strict latency and throughput constraints.
By Yatong Xiao, Yibo Sheng, Lixiang Wang, Mingjie Sun, Yunhao Sha, Pengfei Zhang, Shengyu Wang, Peng Wang, Wenjin Wu, Peng Jiang
arXiv:2610.34917v1 Announce Type: new
Abstract: Diffusion models enable multiple advertisers to compete for exposure within a shared generated image. Yet existing approaches are computationally costl...
By Yijie Su, Ruiyang Si, Tonghan Wang
arXiv:2607. 17281v1 Announce Type: cross Abstract: Auto-bidding plays an essential role in online advertising, automatically adjusting bids for advertisers to optimize their commercial goals.
By Yuejia Dou, Hesong Wang, Xinyu Zhang, Tianyu Wang, Zhilin Zhang, Chuan Yu, Jian Xu, Bo Zheng, Qi Qi