  Self-supervised pre-training of large-scale transformer models on text
corpora followed by finetuning has achieved state-of-the-art on a number of
natural language processing tasks. Recently, Lu et al. (2021, arXiv:2103.05247)
claimed that frozen pretrained transformers (FPTs) match or outperform training
from scratch as well as unfrozen (fine-tuned) pretrained transformers in a set
of transfer tasks to other modalities. In our work, we find that this result
is, in fact, an artifact of not tuning the learning rates. After carefully
redesigning the empirical setup, we find that when tuning learning rates
properly, pretrained transformers do outperform or match training from scratch
in all of our tasks, but only as long as the entire model is finetuned. Thus,
while transfer from pretrained language models to other modalities does indeed
provide gains and hints at exciting possibilities for future work, properly
tuning hyperparameters is important for arriving at robust findings.
