Regret-aware Adaptive Scheduling for Resource-Constrained Auto-Bidding

Frequent decision updates are crucial in dynamic environments, yet strict computational resource constraints severely restrict execution frequency. Existing methods for managing this trade-off either fail to align with horizon-wide system utility, rely on inflexible schedules, or incur profound search complexity through coupled action-timing exploration. To address these limitations, we propose AIGB-ReAL (Regret-Aware Adaptive ScheduLing), a novel framework for resource-constrained temporal scheduling. Conceptually, an unconstrained system can optimize actions across the horizon at every time step, whereas a limited compute budget requires merging adjacent steps to share the same action. We introduce Merge Regret to quantify the resulting reduction in optimal cumulative value after global re-optimization and estimate it using a Decision Landscape. For auto-bidding, this landscape is represented by volume-price response curves, guiding low-regret merging and responsive triggering with reduced reliance on joint exploration. Extensive experiments in simulated and real-world advertising systems demonstrate the superiority of our approach, achieving near-oracle performance with significantly fewer updates.