{"ID":2883112,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.08687","arxiv_id":"2508.08687","title":"Expert-Guided Diffusion Planner for Auto-Bidding","abstract":"Auto-bidding is widely used in advertising systems, serving a diverse range of advertisers. Generative bidding is increasingly gaining traction due to its strong planning capabilities and generalizability. Unlike traditional reinforcement learning-based bidding, generative bidding does not depend on the Markov Decision Process (MDP), thereby exhibiting superior planning performance in long-horizon scenarios. Conditional diffusion modeling approaches have shown significant promise in the field of auto-bidding. However, relying solely on return as the optimality criterion is insufficient to guarantee the generation of truly optimal decision sequences, as it lacks personalized structural information. Moreover, the auto-regressive generation mechanism of diffusion models inherently introduces timeliness risks. To address these challenges, we introduce a novel conditional diffusion modeling approach that integrates expert trajectory guidance with a skip-step sampling strategy to improve generation efficiency. The efficacy of this method has been demonstrated through comprehensive offline experiments and further substantiated by statistically significant outcomes in online A/B testing, yielding an 11.29% increase in conversions and a 12.36% growth in revenue relative to the baseline.","short_abstract":"Auto-bidding is widely used in advertising systems, serving a diverse range of advertisers. Generative bidding is increasingly gaining traction due to its strong planning capabilities and generalizability. Unlike traditional reinforcement learning-based bidding, generative bidding does not depend on the Markov Decision...","url_abs":"https://arxiv.org/abs/2508.08687","url_pdf":"https://arxiv.org/pdf/2508.08687v2","authors":"[\"Yunshan Peng\",\"Wenzheng Shu\",\"Jiahao Sun\",\"Yanxiang Zeng\",\"Jinan Pang\",\"Wentao Bai\",\"Yunke Bai\",\"Xialong Liu\",\"Peng Jiang\"]","published":"2025-08-12T07:23:51Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.IR\"]","methods":"[\"Reinforcement Learning\",\"Diffusion Model\"]","has_code":false}