  Despite the dominance and effectiveness of scaling, resulting in large
networks with hundreds of billions of parameters, the necessity to train
overparameterized models remains poorly understood, while training costs grow
exponentially. In this paper, we explore parameter-efficient training
techniques as an approach to training large neural networks. We introduce a
novel method called ReLoRA, which utilizes low-rank updates to train high-rank
networks. We apply ReLoRA to training transformer language models with up to
1.3B parameters and demonstrate comparable performance to regular neural
network training. ReLoRA saves up to 5.5Gb of RAM per GPU and improves training
speed by 9-40% depending on the model size and hardware setup. Our findings
show the potential of parameter-efficient techniques for large-scale
pre-training.

Computation and Language;Machine Learning