Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models
<|reference_start|>Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models: As language models have scaled both their number of parameters and pretraining dataset sizes, the computational cost for pretraining has become intractable except for the most well-resourced teams. This increasing cost makes it ever more important to be able to reuse a model after it has completed pretraining; allowing for a model's abilities to further improve without needing to train from scratch. In this work, we detail a set of guidelines that cover how to design efficacious data distributions and learning rate schedules for continued pretraining of language models. When applying these findings within a continued pretraining run on top of a well-trained 15B parameter model, we show an improvement of 9\% in average model accuracy compared to the baseline of continued training on the pretraining set. The resulting recipe provides a practical starting point with which to begin developing language models through reuse rather than retraining.<|reference_end|>
@article{parmar2024reuse,,
    title={Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language
  Models},
    author={Jupinder Parmar, Sanjev Satheesh, Mostofa Patwary, Mohammad Shoeybi,
  Bryan Catanzaro},
    journal={arXiv preprint arXiv:2407.07263},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07263},
    primaryClass={cs.CL}
}