SEGB: Self-Evolved Generative Bidding with Local Autoregressive Diffusion
Yulong Gao, Wan Jiang, Mingzhe Cao, Xuepu Wang, Zeyu Pan, Haonan Yang, Ye Liu, Xin Yang

TL;DR
SEGB introduces a self-evolving, offline reinforcement learning framework for online ad bidding that synthesizes future states and refines policies without external data, significantly improving performance and business value.
Contribution
The paper presents SEGB, a novel offline planning and self-evolution framework for generative bidding that enhances policy effectiveness without external simulation or expert input.
Findings
Outperforms state-of-the-art baselines on AuctionNet benchmark
Achieves +10.19% increase in target cost in large-scale deployment
Demonstrates robust policy improvement solely from static data
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…
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Taxonomy
TopicsConsumer Market Behavior and Pricing · Auction Theory and Applications · Generative Adversarial Networks and Image Synthesis
