Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation

Recent developments in NLP have been accompanied by large, expensive models. Knowledge distillation is the standard method to realize these gains in applications with limited resources: a compact student is trained to recover the outputs of a powerful teacher. While most prior work investigates student architectures and transfer techniques, we focus on an often-neglected aspect---student initialization. We argue that a random starting point hinders students from fully leveraging the teacher expertise, even in the presence of a large transfer set. We observe that applying language model pre-training to students unlocks their generalization potential, surprisingly even for very compact networks. We conduct experiments on 4 NLP tasks and 24 sizes of Transformer-based students; for sentiment classification on the Amazon Book Reviews dataset, pre-training boosts size reduction and TPU speed-up from 3.1x/1.25x to 31x/16x. Extensive ablation studies dissect the interaction between pre-training and distillation, revealing a compound effect even when they are applied on the same unlabeled dataset.