Distillation Scaling Laws
Authors: name: Dan Busbridge, name: Amitis Shidani, name: Floris Weers, name: Jason Ramapuram, name: Etai Littwin, name: Russ Webb

We provide a distillation scaling law that estimates distilled model
performance based on a compute budget and its allocation between the student
and teacher. Our findings reduce the risks associated with using distillation
at scale; compute allocation for both the teacher and student models can now be
done to maximize student performance. We provide compute optimal distillation
recipes for when 1) a teacher exists, or 2) a teacher needs training. If many
students are to be distilled, or a teacher already exists, distillation
outperforms supervised pretraining until a compute level which grows
predictably with student size. If one student is to be distilled and a teacher
also needs training, supervised learning should be done instead. Additionally,
we provide insights across our large scale study of distillation, which
increase our understanding of distillation and inform experimental design.