Curriculum Learning: A Regularization Method for Efficient and Stable Billion-Scale GPT Model Pre-Training

Recent works have demonstrated great success in training high-capacity autoregressive language models (GPT, GPT-2, GPT-3) on a huge amount of unlabeled text corpus for text generation. Despite showing great results, this generates two training efficiency challenges. First, training large corpora can be extremely timing consuming, and how to present training samples to the model to improve the token-wise convergence speed remains a challenging and open question. Second, many of these large models have to be trained with hundreds or even thousands of processors using data-parallelism with a very large batch size. Despite of its better compute efficiency, it has been observed that large-batch training often runs into training instability issue or converges to solutions with bad generalization performance. To overcome these two challenges, we present a study of a curriculum learning based approach, which helps improves the pre-training convergence speed of autoregressive models. More importantly, we find that curriculum learning, as a regularization method, exerts a gradient variance reduction effect and enables to train autoregressive models with much larger batch sizes and learning rates without training instability, further improving the training speed. Our evaluations demonstrate that curriculum learning enables training GPT-2 models (with up to 1.5B parameters) with 8x larger batch size and 4x larger learning rate, whereas the baseline approach struggles with training divergence. To achieve the same validation perplexity targets during pre-training, curriculum learning reduces the required number of tokens and wall clock time by up to 59% and 54%, respectively. To achieve the same or better zero-shot WikiText-103/LAMBADA evaluation results at the end of pre-training, curriculum learning reduces the required number of tokens and wall clock time by up to 13% and 61%, respectively.