SEGB: Self-Evolved Generative Bidding with Local Autoregressive Diffusion

• URL: http://arxiv.org/abs/2602.22226v1
• Date: Wed, 31 Dec 2025 09:05:59 GMT
• Title: SEGB: Self-Evolved Generative Bidding with Local Autoregressive Diffusion
• Authors: Yulong Gao, Wan Jiang, Mingzhe Cao, Xuepu Wang, Zeyu Pan, Haonan Yang, Ye Liu, Xin Yang,
• Abstract summary: Self-Evolved Generative Bidding (SEGB) is a framework that plans proactively and refines itself entirely offline.<n>SEGB first synthesizes plausible short-horizon future states to guide each bid, providing the agent with crucial, dynamic foresight.<n>It then performs value-guided policy refinement to iteratively discover superior strategies without any external intervention.
• Score: 9.051746879211764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the realm of online advertising, automated bidding has become a pivotal tool, enabling advertisers to efficiently capture impression opportunities in real-time. Recently, generative auto-bidding has shown significant promise, offering innovative solutions for effective ad optimization. However, existing offline-trained generative policies lack the near-term foresight required for dynamic markets and usually depend on simulators or external experts for post-training improvement. To overcome these critical limitations, we propose Self-Evolved Generative Bidding (SEGB), a framework that plans proactively and refines itself entirely offline. SEGB first synthesizes plausible short-horizon future states to guide each bid, providing the agent with crucial, dynamic foresight. Crucially, it then performs value-guided policy refinement to iteratively discover superior strategies without any external intervention. This self-contained approach uniquely enables robust policy improvement from static data alone. Experiments on the AuctionNet benchmark and a large-scale A/B test validate our approach, demonstrating that SEGB significantly outperforms state-of-the-art baselines. In a large-scale online deployment, it delivered substantial business value, achieving a +10.19% increase in target cost, proving the effectiveness of our advanced planning and evolution paradigm.

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