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We propose a simple and efficient approach for training the BERT model. Ourapproach exploits the special structure of BERT that contains a stack ofrepeated modules (i.e., transformer encoders). Our proposed approach firsttrains BERT with the weights shared across all the repeated modules till somepoint. This is for learning the commonly shared component of weights across allrepeated layers. We then stop weight sharing and continue training untilconvergence. We present theoretic insights for training by sharing weights thenunsharing with analysis for simplified models. Empirical experiments on theBERT model show that our method yields better performance of trained models,and significantly reduces the number of training iterations.

Speeding up Deep Model Training by Sharing Weights and Then Unsharing