用验证反馈动态调整跨任务数据权重，提升小规模广告竞价模型性能
VAO: Validation-Aligned Optimization for Cross-Task Generative Auto-Bidding
- 基于验证表现自适应重加权跨任务数据，减少分布偏移带来的梯度偏差
- 在标准基准上实现优于基线的跨任务泛化性能，尤其在数据稀缺场景下优势显著
- 适合需要多任务学习且数据量有限的在线广告系统开发者
生成式自动出价在在线广告中表现优异，但在参与广告主较少的小规模场景下常面临数据稀缺问题。尽管跨任务数据共享是自然解决方案，但传统方法因任务间分布差异引入梯度偏差，且不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调整跨任务数据贡献的方法。VAO通过将训练动态对齐至目标任务泛化能力提升，有效利用辅助数据并缓解梯度偏差。基于VAO，我们构建了一个统一的生成式自动出价框架，使用单个模型和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该方法的有效性。
原文摘要 · Abstract (English)
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.
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