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

• URL: http://arxiv.org/abs/2509.15927v3
• Date: Wed, 08 Oct 2025 14:06:32 GMT
• Title: Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search
• Authors: Zhiyu Mou, Yiqin Lv, Miao Xu, Qi Wang, Yixiu Mao, Qichen Ye, Chao Li, Rongquan Bai, Chuan Yu, Jian Xu, Bo Zheng,
• Abstract summary: Auto-bidding serves as a critical tool for advertisers to improve their performance.<n>Recent progress has demonstrated that AI-Generated Bidding (AIGB) achieves superior performance compared to typical offline reinforcement learning (RL)-based auto-bidding methods.<n>We propose AIGB-Pearl, a novel method that integrates generative planning and policy optimization.
• Score: 24.02739832976663
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Auto-bidding serves as a critical tool for advertisers to improve their 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 offline dataset. To address this, we propose {AIGB-Pearl} (\emph{{P}lanning with {E}valu{A}tor via RL}), a novel method that integrates generative planning and policy optimization. The core of AIGB-Pearl lies in constructing a trajectory evaluator for scoring generation quality and designing a provably sound KL-Lipschitz-constrained score maximization scheme to ensure safe and efficient exploration beyond the offline dataset. A practical algorithm incorporating the synchronous coupling technique is further devised to ensure the model regularity required by the proposed scheme. Extensive experiments on both simulated and real-world advertising systems demonstrate the state-of-the-art performance of our approach.

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