Reinforcement Learning for Controller Parameters in the Auto-Bidding System at Pinterest
arXiv:2610.33248v1 Announce Type: new Abstract: Auto-bidding systems in online advertising must balance competing objectives-maximizing budget delivery while minimizing cost per action-across heterogeneous campaigns under non-stationary market conditions. This trade-off evolves over campaign lifespans: early on, cost optimization dominates when budgets roll over; near deadlines, budget delivery becomes urgent. While PID controllers are widely used in production for their stability and interpretability, fixed tunings cannot adapt to diverse campaigns and shifting traffic patterns. We present RL
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本站仅作技术动态聚合，不构成任何形式的建议。