Language models scale reliably with over-training and on downstream
  tasks
Authors: name: Samir Yitzhak Gadre, name: Georgios Smyrnis, name: Vaishaal Shankar, name: Suchin Gururangan, name: Mitchell Wortsman, name: Rulin Shao, name: Jean Mercat, name: Alex Fang, name: Jeffrey Li, name: Sedrick Keh, name: Rui Xin, name: Marianna Nezhurina, name: Igor Vasiljevic, name: Jenia Jitsev, name: Alexandros G. Dimakis, name: Gabriel Ilharco, name: Shuran Song, name: Thomas Kollar, name: Yair Carmon, name: Achal Dave, name: Reinhard Heckel, name: Niklas Muennighoff, name: Ludwig Schmidt

Scaling laws are useful guides for developing language models, but there are
still gaps between current scaling studies and how language models are
ultimately trained and evaluated. For instance, scaling is usually studied in
the compute-optimal training regime (i.e., "Chinchilla optimal" regime);
however, in practice, models are often over-trained to reduce inference costs.
Moreover, scaling laws mostly predict loss on next-token prediction, but
ultimately models are compared based on downstream task performance. In this
paper, we address both shortcomings. To do so, we create a testbed of 104
models with 0.011B to 6.9B parameters trained with various numbers of tokens on
three data distributions. First, we investigate scaling in the over-trained
regime. We fit scaling laws that extrapolate in both the number of model
parameters and the ratio of training tokens to parameters. This enables us to
predict the validation loss of a 1.4B parameter, 900B token run (i.e.,
32times over-trained) and a 6.9B parameter, 138B token
runx2014each from experiments that take 300times less compute.
Second, we relate the perplexity of a language model to its downstream task
performance via a power law. We use this law to predict top-1 error averaged
over downstream tasks for the two aforementioned models using experiments that
take 20times less compute. Our experiments are available at
https://github.com/mlfoundations/scaling.