Generative auto-bidding has demonstrated strong performance in online advertising, yet it often suffers from data scarcity in small-scale settings with limited advertiser participation. While cross-task data sharing is a natural remedy to mitigate this issue, naive approaches often introduce gradient bias due to distribution shifts across different tasks, and existing methods are not readily applicable to generative auto-bidding. In this paper, we propose Validation-Aligned Optimization (VAO), a principled data-sharing method that adaptively reweights cross-task data contributions based on validation performance feedback. Notably, VAO aligns training dynamics to prioritize updates that improve generalization on the target task, effectively leveraging auxiliary data and mitigating gradient bias. Building on VAO, we introduce a unified generative autobidding framework that generalizes across multiple tasks using a single model and all available task data. Extensive experiments on standard auto-bidding benchmarks validate the effectiveness of our approach.

翻译：生成式自动出价在在线广告中展现出卓越性能，但在广告主参与有限的小规模场景中常受数据稀缺问题困扰。跨任务数据共享是缓解该问题的自然解决方案，但朴素方法常因不同任务间的分布偏移引入梯度偏差，且现有方法难以直接适用于生成式自动出价。本文提出验证对齐优化（VAO），一种基于验证性能反馈自适应重加权跨任务数据贡献的原则性数据共享方法。VAO通过对齐训练动态，优先选择能提升目标任务泛化能力的参数更新，从而有效利用辅助数据并缓解梯度偏差。基于VAO，我们构建了统一的生成式自动出价框架，该框架能够使用单一模型及全部可用任务数据实现多任务泛化。在标准自动出价基准上的大量实验验证了本方法的有效性。