Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912937918070784 |
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| author | Mou, Zhiyu Lv, Yiqin Xu, Miao Wang, Qi Mao, Yixiu Chen, Jinghao Ye, Qichen Li, Chao Bai, Rongquan Yu, Chuan Xu, Jian Zheng, Bo |
| author_facet | Mou, Zhiyu Lv, Yiqin Xu, Miao Wang, Qi Mao, Yixiu Chen, Jinghao Ye, Qichen Li, Chao Bai, Rongquan Yu, Chuan Xu, Jian Zheng, Bo |
| contents | 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 algorithm that incorporates the synchronous coupling technique is further developed 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15927 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search Mou, Zhiyu Lv, Yiqin Xu, Miao Wang, Qi Mao, Yixiu Chen, Jinghao Ye, Qichen Li, Chao Bai, Rongquan Yu, Chuan Xu, Jian Zheng, Bo Machine Learning Artificial Intelligence 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 algorithm that incorporates the synchronous coupling technique is further developed 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. |
| title | Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.15927 |