  Pre-trained language models in the past years have shown exponential growth
in model parameters and compute time. ELECTRA is a novel approach for improving
the compute efficiency of pre-trained language models (e.g. BERT) based on
masked language modeling (MLM) by addressing the sample inefficiency problem
with the replaced token detection (RTD) task. Our work proposes adaptive early
exit strategy to maximize the efficiency of the pre-training process by
relieving the model's subsequent layers of the need to process latent features
by leveraging earlier layer representations. Moreover, we evaluate an initial
approach to the problem that has not succeeded in maintaining the accuracy of
the model while showing a promising compute efficiency by thoroughly
investigating the necessity of the generator module of ELECTRA.
