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
OneBid is a unified auto‑bidding foundation model that consolidates diverse cost‑per‑X (oCPX) advertising scenarios into a single framework. It builds on Decision Transformer by conditioning on two atomic signals—Return‑to‑Go for conversion value and Cost‑to‑Go for cost ratio—and incorporates value‑aware regularization. A sequence‑level Mixture‑of‑Experts architecture captures cross‑scenario knowledge while preserving low latency, and a Critic‑guided Relative Offline Policy optimization (CROP) aligns the backbone with scenario‑specific preferences without unsafe online exploration. In production at Kuaishou, OneBid achieved a 2.2% overall ADVV increase and up to 13.1% in the ROAS scenario.
By Yewen Li, Peng Jiang, Yitian Li, Pengfei Lv, Xialong Liu, Peng Jiang, Qingpeng Cai
The paper introduces the Latent Advertiser Mixture Auction (LAMA), a token‑level advertising framework that integrates advertiser influence directly into the text generation process. Advertisers provide local continuation values that shape next‑token policies, and the platform decodes these through a latent mixture while updating an allocation posterior. LAMA is shown to satisfy Markov DSIC and IR, achieve near‑optimal KL‑regularized welfare, and, in proof‑of‑concept experiments on commercial‑search queries, improve platform welfare and revenue without compromising user‑facing response quality.
By Hanbing Liu, Bowei Zhang, Changyuan Yu, Yinyu Ye, Qi Qi
Diffusion models enable multiple advertisers to compete for exposure within a shared generated image. Yet existing approaches are computationally costly when generating images under varying target-sha...
The paper introduces OGC-DPO, an active preference learning framework for aligning large language models with multiple objectives such as helpfulness, harmlessness, and honesty. OGC-DPO treats each response pair and objective as a separate query, using the model’s implicit gradient to gauge informativeness and selecting queries that expand gradient coverage for each objective. Experiments show that OGC-DPO outperforms existing methods at the same annotation cost and reveals that informative conflicts can serve as valuable training signals.
By Duc Anh Vu, Thong Nguyen, Hoang Pham, Ponhvoan Srey, Luu Anh Tuan
arXiv:2606.09623v2 Announce Type: replace
Abstract: Web platforms run many marketing campaigns at once, and for each one they must decide which products to promote and which users to target. The best...
By Maja Lindstr\"om, Natalija Glisovic, Jan von Pichowski, Tommy L\"ofstedt, Martin Rosvall
arXiv:2608. 13833v1 Announce Type: cross Abstract: Conversational advertising aims to deliver useful ads within multi-turn assistant interactions.
By Simiao Zuo, Chenhui Xu, Yimeng Jia, Qiang Lou, Jian Jiao, Denis Charles