ReLoRA: Efficient Large Model Training Through Low-Rank Updates
This article focuses on reducing the training costs of large Transformer language models. The author introduces a low-rank update-based method called ReLoRA. A core principle in the development of deep learning over the past decade has been to “stack more layers,” and the author aims to explore whether stacking can similarly enhance training efficiency for … Read more