One reason is that RoBERTa was trained on a much larger dataset and using a more effective training procedure.
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Just because of dynamic masking, BERT uses the same masking architecture in each epoch while RoBERTa uses a different/dynamic masking structure in each epoch. This is also the reason for requiring a larger & related dataset to train and get robust results.
RoBERTa outperforms BERT in several ways:
RoBERTa is an improved model over BERT in a number of ways.
These enhancements include the elimination of the next sentence prediction target, the use of dynamic masking during training, training on larger datasets for longer periods of time, and the use of more advanced optimization approaches.
RoBERTa also has more parameters than BERT, which makes it possible for it to effectively capture complicated linguistic patterns.
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