Generative Auto-Bidding with Unified Modeling and Exploration
Mingming Zhang, Feiqing Zhuang, Na Li, Shengjie Sun, Xiaowei Chen, Junxiong Zhu, Fei Xiao, Keping Yang, Lixin Zou, Chenliang Li

TL;DR
GUIDE introduces a unified framework combining generative modeling, exploration, and safety mechanisms for automated bidding, significantly improving performance in digital advertising.
Contribution
The paper presents GUIDE, a novel approach integrating directed exploration with a safe fallback using a Decision Transformer, Q-value guidance, and inverse dynamics modules.
Findings
GUIDE outperforms state-of-the-art baselines in experiments.
Real-world deployment yields +4.10% GMV and +3.52% ROI improvements.
GUIDE demonstrates effectiveness and industrial applicability.
Abstract
Automated bidding is central to modern digital advertising. Early rule-based methods lacked adaptability, while subsequent Reinforcement Learning approaches modeled bidding as a Markov Decision Process but struggled with long-term dependencies. Recent generative models show promise, yet they lack explicit mechanisms to balance exploration and safety, relying solely on action perturbations or trajectory guidance without a safety fallback. This results in inefficient exploration and elevated financial risk for advertising platforms. To address this gap, we propose GUIDE (Generative Auto-Bidding with Unified Modeling and Exploration), a framework that synergistically integrates directed exploration with a safe fallback mechanism. GUIDE employs a Decision Transformer (DT) to jointly model historical bidding actions and environmental state transitions. A Q-value module guides the DT's…
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