Generalizable Multi-Pass Training of Ads Recommendation Models with Foundation Model Guidance

High-capacity Click-Through Rate (CTR) models for ads recommendation often exhibit pronounced one-epoch overfitting: performance peaks after a single training pass (epoch) over the data, while additional epochs degrade generalization as the model memorizes noise in high-variance click labels. To address this challenge, we propose TeMPO, a principled framework for generalizable multi-pass training of recommendation models that leverages supervision from a teacher recommendation Foundation Model (FM). Our key idea is to move beyond maximizing the conditional likelihood of noisy click labels. Instead, we maximize the joint likelihood of observing both click labels and the teacher's rich representational knowledge, combining the task loss with teacher-guided alignment objectives. We propose a two-stage optimization strategy to stabilize multi-pass learning. Our theoretical analysis shows that distillation acts as complexity regularization, yielding tighter generalization bounds than standard empirical risk minimization under noisy labels. Extensive experiments on public benchmarks and Meta's production-scale ads application demonstrate that TeMPO enables additional training passes to improve, rather than degrade, performance.