AMINO: Generative Auto-bidding with Multimodal Trajectory Modeling and Initial Noise Optimization

Auto-bidding is a critical technique in online advertising for achieving advertisers' commercial goals under constraints. Recent generative auto-bidding methods have shown strong potential by learning bidding policies directly from large-scale offline data. In this setting, auto-bidding trajectories are inherently multimodal, making conditional diffusion particularly suitable for trajectory modeling. However, our theoretical analysis reveals that conditional diffusion can suffer from mode concentration under the high target returns commonly used to steer generation toward high-value trajectories, potentially resulting in suboptimal bidding trajectories. Moreover, existing diffusion-based methods lack an explicit mechanism to exploit task feedback and improve beyond static offline data. We therefore propose AMINO (generative Auto-bidding with Multimodal trajectory modeling and Initial Noise Optimization), a novel framework that decouples performance optimization from multimodal trajectory modeling. Rather than modifying the diffusion planner, AMINO keeps it fixed and learns an initial noise policy via reinforcement learning for performance optimization. To address the challenges of delayed feedback and offline optimization in auto-bidding, we develop a tailored noise optimization pipeline, enabling reliable trajectory-level feedback and stable exploration in the continuous noise space. Extensive experiments on a large-scale real-world dataset demonstrate the state-of-the-art performance of AMINO.