Self-Distillation for Data-Scarce Language Model Pretraining

This research paper investigates self-distillation as a powerful regularization technique for pretraining language models when high-quality data is in short supply. By comparing various training strategies across different model scales and data scarcity levels, the authors demonstrate that self-distillation significantly outperforms both direct training and standard methods like weight decay or exponential moving averages. The study identifies a specific crossover threshold where distillation becomes superior, particularly when the available data is less than one-fourth of the amount prescribed by Chinchilla scaling laws. Practical results suggest that using larger models with natural teacher temperatures provides the most effective supervision, preventing...