  Distillation from Weak Teacher (DWT) is a method of transferring knowledge
from a smaller, weaker teacher model to a larger student model to improve its
performance. Previous studies have shown that DWT can be effective in the
vision domain and natural language processing (NLP) pre-training stage.
Specifically, DWT shows promise in practical scenarios, such as enhancing new
generation or larger models using pre-trained yet older or smaller models and
lacking a resource budget. However, the optimal conditions for using DWT have
yet to be fully investigated in NLP pre-training. Therefore, this study
examines three key factors to optimize DWT, distinct from those used in the
vision domain or traditional knowledge distillation. These factors are: (i) the
impact of teacher model quality on DWT effectiveness, (ii) guidelines for
adjusting the weighting value for DWT loss, and (iii) the impact of parameter
remapping as a student model initialization technique for DWT.
