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This paper investigates training-time data augmentation techniques to mitigate overfitting in autoregressive language model pretraining under data-constrained, compute-abundant regimes, finding that combining token-level noise, sequence permutations, and target offset prediction improves validation loss.
This paper studies data-constrained language model pretraining, proposing masked-input regularization (MIR) to improve validation loss and downstream performance, and SoftQ, a scaling law that better captures model-data interaction under repeated data.