A Unified Multi-Task Learning Framework for Generative Auto-Bidding with Validation-Aligned Optimization
This work addresses computational and data efficiency challenges for advertisers in volatile online advertising environments, though it appears incremental as it builds on existing multi-task learning approaches.
The paper tackles the problem of inefficient independent optimization of multiple bidding tasks in online advertising by proposing a multi-task learning framework that aligns training with validation objectives, resulting in significant performance improvements in both simulated and real-world systems.
In online advertising, heterogeneous advertiser requirements give rise to numerous customized bidding tasks that are typically optimized independently, resulting in extensive computation and limited data efficiency. Multi-task learning offers a principled framework to train these tasks jointly through shared representations. However, existing multi-task optimization strategies are primarily guided by training dynamics and often generalize poorly in volatile bidding environments. To this end, we present Validation-Aligned Multi-task Optimization (VAMO), which adaptively assigns task weights based on the alignment between per-task training gradients and a held-out validation gradient, thereby steering updates toward validation improvement and better matching deployment objectives. We further equip the framework with a periodicity-aware temporal module and couple it with an advanced generative auto-bidding backbone to enhance cross-task transfer of seasonal structure and strengthen bidding performance. Meanwhile, we provide theoretical insights into the proposed method, e.g., convergence guarantee and alignment analysis. Extensive experiments on both simulated and large-scale real-world advertising systems consistently demonstrate significant improvements over typical baselines, illuminating the effectiveness of the proposed approach.