  We study empirical scaling laws for transfer learning between distributions
in an unsupervised, fine-tuning setting. When we train increasingly large
neural networks from-scratch on a fixed-size dataset, they eventually become
data-limited and stop improving in performance (cross-entropy loss). When we do
the same for models pre-trained on a large language dataset, the slope in
performance gains is merely reduced rather than going to zero. We calculate the
effective data "transferred" from pre-training by determining how much data a
transformer of the same size would have required to achieve the same loss when
training from scratch. In other words, we focus on units of data while holding
everything else fixed. We find that the effective data transferred is described
well in the low data regime by a power-law of parameter count and fine-tuning
dataset size. We believe the exponents in these power-laws correspond to
measures of the generality of a model and proximity of distributions (in a
directed rather than symmetric sense). We find that pre-training effectively
multiplies the fine-tuning dataset size. Transfer, like overall performance,
scales predictably in terms of parameters, data, and compute.
