JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911573630517248 |
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| author | Meng, Linghui Gan, Chun Niu, Shengsheng Zhang, Chengcheng Li, Chenchen Yang, Chuan Mao, Yi Zhu, Xin He, Jie Lin, Zhangang Law, Ching |
| author_facet | Meng, Linghui Gan, Chun Niu, Shengsheng Zhang, Chengcheng Li, Chenchen Yang, Chuan Mao, Yi Zhu, Xin He, Jie Lin, Zhangang Law, Ching |
| contents | Auto-bidding services optimize real-time bidding strategies for advertisers under key performance indicator (KPI) constraints such as target return on investment and budget. However, uncertainties such as model prediction errors and feedback latency can cause bidding strategies to deviate from ex-post optimality, leading to inefficient allocation. To address this issue, we propose JD-BP, a Joint generative Decision framework for Bidding and Pricing. Unlike prior methods, JD-BP jointly outputs a bid value and a pricing correction term that acts additively with the payment rule such as GSP. To mitigate adverse effects of historical constraint violations, we design a memory-less Return-to-Go that encourages future value maximizing of bidding actions while the cumulated bias is handled by the pricing correction. Moreover, a trajectory augmentation algorithm is proposed to generate joint bidding-pricing trajectories from a (possibly arbitrary) base bidding policy, enabling efficient plug-and-play deployment of our algorithm from existing RL/generative bidding models. Finally, we employ an Energy-Based Direct Preference Optimization method in conjunction with a cross-attention module to enhance the joint learning performance of bidding and pricing correction. Offline experiments on the AuctionNet dataset demonstrate that JD-BP achieves state-of-the-art performance. Online A/B tests at JD.com confirm its practical effectiveness, showing a 4.70% increase in ad revenue and a 6.48% improvement in target cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05845 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing Meng, Linghui Gan, Chun Niu, Shengsheng Zhang, Chengcheng Li, Chenchen Yang, Chuan Mao, Yi Zhu, Xin He, Jie Lin, Zhangang Law, Ching Computer Science and Game Theory Machine Learning 91B26 I.2.6; H.3.5 Auto-bidding services optimize real-time bidding strategies for advertisers under key performance indicator (KPI) constraints such as target return on investment and budget. However, uncertainties such as model prediction errors and feedback latency can cause bidding strategies to deviate from ex-post optimality, leading to inefficient allocation. To address this issue, we propose JD-BP, a Joint generative Decision framework for Bidding and Pricing. Unlike prior methods, JD-BP jointly outputs a bid value and a pricing correction term that acts additively with the payment rule such as GSP. To mitigate adverse effects of historical constraint violations, we design a memory-less Return-to-Go that encourages future value maximizing of bidding actions while the cumulated bias is handled by the pricing correction. Moreover, a trajectory augmentation algorithm is proposed to generate joint bidding-pricing trajectories from a (possibly arbitrary) base bidding policy, enabling efficient plug-and-play deployment of our algorithm from existing RL/generative bidding models. Finally, we employ an Energy-Based Direct Preference Optimization method in conjunction with a cross-attention module to enhance the joint learning performance of bidding and pricing correction. Offline experiments on the AuctionNet dataset demonstrate that JD-BP achieves state-of-the-art performance. Online A/B tests at JD.com confirm its practical effectiveness, showing a 4.70% increase in ad revenue and a 6.48% improvement in target cost. |
| title | JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing |
| topic | Computer Science and Game Theory Machine Learning 91B26 I.2.6; H.3.5 |
| url | https://arxiv.org/abs/2604.05845 |