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Scaling Repeated-Data Pretraining: Batch Size, Regularization, and GPU-Time Tradeoffs
We study pretraining when compute is abundant but unique training data are finite, requiring repeated training on the same data. We first revisit infinite-compute pretraining (Kim et al. 2026) and show that its frontier depends critically on choices previously fixed or studied only in isolation. In
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