[2509.15927] Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search

Computer Science > Machine Learning arXiv:2509.15927 (cs) [Submitted on 19 Sep 2025 (v1), last revised 3 Mar 2026 (this version, v4)] Title:Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search Authors:Zhiyu Mou, Yiqin Lv, Miao Xu, Qi Wang, Yixiu Mao, Jinghao Chen, Qichen Ye, Chao Li, Rongquan Bai, Chuan Yu, Jian Xu, Bo Zheng View a PDF of the paper titled Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search, by Zhiyu Mou and 11 other authors View PDF HTML (experimental) Abstract:Auto-bidding is a critical tool for advertisers to improve advertising performance. Recent progress has demonstrated that AI-Generated Bidding (AIGB), which learns a conditional generative planner from offline data, achieves superior performance compared to typical offline reinforcement learning (RL)-based auto-bidding methods. However, existing AIGB methods still face a performance bottleneck due to their inherent inability to explore beyond the static dataset with feedback. To address this, we propose \textbf{AIGB-Pearl} (\emph{\textbf{P}lanning with \textbf{E}valu\textbf{A}tor via \textbf{RL}}), a novel method that integrates generative planning and policy optimization. The core of AIGB-Pearl lies in constructing a trajectory evaluator to assess the quality of generated scores and designing a provably sound KL-Lipschitz-constrained score-maximization scheme to ensure safe and efficient exploration beyond the offline dataset. A practical...