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On-policy distillation (OPD) improves language-model post-training by letting the student learn from its own sampled trajectories under teacher supervision, aligning the training process with inference-time behavior. However, the rollout policy in OPD is usually treated as fixed: each round samples directly from the cu...
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,...
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