[I am not an expert, but I believe this is right]
If you need to add different tokens, then you will need to train the RoBerta model from scratch.
(You probably don’t want to do that.)
It doesn’t work to change the tokens after the model has been pre-trained.
Do you definitely need to add different tokens?
If you just include your different tokens in your data, the tokenizer will probably deal with them OK, by representing them as combinations or tokens it already knows.
I recommend Chris McCormick’s blog posts about this, BERT Word Embeddings Tutorial · Chris McCormick 44
By default, if you fine-tune a pre-trained RoBerta model, then the embedding layer will be very slightly tuned.  Most of the tuning change will happen in the last few layers, especially the Classification head layer.
If you want to tune ONLY the last layer(s), you can Freeze the earlier layers.   (It isn’t possible to freeze the later layers and tune the earlier ones).