hey @harish3110 this is a great question!
indeed, one can always fine-tune the pretrained language model on your corpus before performing the supervised task (e.g. text classification).
in my experience, the gains in accuracy are strongly dependent on how different your corpus is from the one used during pretraining, e.g. if your corpus is something quirky like source code 1 then fine-tuning the language model first can be quite beneficial since the target domain is quite different from say wikipedia.
having said that, what i usually do in practice is run a few supervised fine-tuning experiments first and then think about where fine-tuning the language model is needed (since it takes longer to train).
hth!