arXiv Machine Learning

JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing

arXiv:2604. 05845v2 Announce Type: replace-cross Abstract: Auto-bidding services optimize real-time bidding strategies for advertisers under key performance indicator (KPI) constraints such as target return on investment and budget.

arXiv Machine Learning

1d ago

PRO-Bid: Pareto-Prioritized Regret Optimization for Constraint-Aware Generative Auto-Bidding

arXiv:2602. 08261v2 Announce Type: replace Abstract: Auto-bidding systems strive to maximize marketing value while maintaining high compliance with efficiency constraints, such as Target Cost-Per-Action (CPA).

By Binglin Wu, Yingyi Zhang, Xianneng Li, Ruyue Deng, Chuan Yue, Weiru Zhang, Xiaoyi Zeng

llms benchmarks

arXiv Machine Learning

1d ago

Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions

arXiv:2605. 09448v2 Announce Type: replace Abstract: We study the operational problem of automated bidding in repeated first-price auctions under budget and return-on-spend (RoS) constraints.

By Zihao Hu, Yuxiao Wen, Yuan Yao, Jiheng Zhang, Zhengyuan Zhou

arXiv AI

Jul 21

AIGB-R1: Self-Evolving Generative Auto-Bidding via Hierarchical Planner-Executor Optimization

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

llms agents efficiency safety

arXiv AI

1d ago

Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies

arXiv:2601. 02754v3 Announce Type: replace-cross Abstract: With the rapid development of e-commerce, auto-bidding has become a key asset in optimizing advertising performance under diverse advertiser environments.

By Mingming Zhang, Na Li, Zhuang Feiqing, Hongyang Zheng, Jiangbing Zhou, Wang Wuyin, Sheng-jie Sun, XiaoWei Chen, Junxiong Zhu, Lixin Zou, Chenliang Li

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jul 31

PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective

arXiv:2607. 27265v1 Announce Type: new Abstract: Real-time bidding is central to computational advertising, comprising three elements: Supply Side Platform (SSP) selling ad impressions, Demand Side Platform (DSP) bidding for advertisers, and Ad Exchange conducting auctions between them.

By Shengtian Yang, Yewen Li, Peng Jiang, Zhiyi Lyu, Bo An, Peng Jiang, Qingpeng Cai, Lei Feng

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 15

DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation

arXiv:2606. 14192v1 Announce Type: new Abstract: Auto-bidding is a core component of real-time advertising systems, where decisions must optimize long-term performance under budget and cost constraints, while online exploration is prohibitively risky.

By Miduo Cui, Haochen Wang, Shangqin Mao, Xun Yang, Qianlong Xie, Xingxing Wang, Xuri Ge, Ying Zhou, Zhiwei Xu

llms rag reinforcement-learning benchmarks

arXiv AI

Jun 19

Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search

arXiv:2509. 15927v5 Announce Type: replace-cross Abstract: Auto-bidding is a critical tool for advertisers to improve advertising performance.

By Zhiyu Mou, Yiqin Lv, Miao Xu, Qi Wang, Yixiu Mao, Jinghao Chen, Qichen Ye, Chao Li, Rongquan Bai, Chuan Yu, Jian Xu, Bo Zheng

reinforcement-learning benchmarks

arXiv AI

Jul 29

HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising

arXiv:2607. 24779v1 Announce Type: new Abstract: Online advertising bidding systems typically deploy multiple offline-trained expert models (e.

By Ji Wu, Yunshan Peng, Wentao Bai, Yunke Bai, Wenzheng Shu, Jinan Pang, Yanxiang Zeng, Xialong Liu

llms agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 26

HOB: A Holistically Optimized Bidding Strategy under Heterogeneous Bidding Environments

arXiv:2510. 15238v2 Announce Type: replace-cross Abstract: Optimizing a single advertising campaign across heterogeneous channels is a central challenge in industrial autobidding.

By Qi Li, Wendong Huang, Qichen Ye, Wutong Xu, Cheems Wang, Wei Yuan, Miao Xu, Zhiyu Mou, Guan Wang, Rongquan Bai, Chuan Yu, Jian Xu

benchmarks

arXiv Machine Learning

Aug 4

LLM-OSDA: An Optimal-Stopping Dynamic Auction for Native Advertising in Multi-Turn LLM Conversations

arXiv:2608. 00123v1 Announce Type: cross Abstract: LLM-native advertising embeds sponsored content directly into model-generated responses, shifting the unit of sale from a fixed slot to a moment within an evolving conversation.

By Yan Fang, Jialin Chen, Chun Gan, Hang Yu, Mingjun Nie, Yeyu Zhang, Fengxiang He, Ching Law

llms

arXiv Machine Learning

Jun 30

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions

arXiv:2606. 28943v1 Announce Type: cross Abstract: Learning to bid in repeated multi-unit auctions with bandit feedback poses a fundamental challenge.

By Junhan Li, Yuxin Zhang, Haoran Wang, Minghao Chen

reinforcement-learning benchmarks safety

arXiv AI

Jun 10

HMAF: A Hierarchical Multi-Slot GD-RTB Allocation Framework

arXiv:2606. 09896v1 Announce Type: cross Abstract: In modern online advertising platforms, Guaranteed Delivery (GD) contracts coexist and bid with Real-Time Bidding (RTB) auctions.

By Tianxing Bu, Zhaoqi Zhang, Linyou Cai, Miao Xie, Shengri Xue, Tan Qu, Qianlong Xie, Xingxing Wang, Siqiang Luo, Gao Cong

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