Tokens start as a special [MASK] token. Then as the diffusion process runs they are "unmasked" i.e. sampled.

So yes, you define a sequence of [MASK] tokens with some length ahead of time.

In practice, if a model wants to write a shorter sequence, it'll just fill the remaining tokens with empty content. If it wants to write a longer sequence, you'll have to identify this and extend the sequence with more [MASK] tokens. This is typically obvious since there's no "end of sequence" token present if the model wants to generate more.

Yes, this is the case. During training, the model will get a sequence of text (ex, 512 tokens long) with a percentage of them masked out (with a special <MASK> token). It learns how to unmask those tokens to construct the original text.

In the case that you mentioned, if we had 4 <MASK> tokens in a row, all we are doing for decoding is predicting what those 4 tokens should be.

Generally, this does not seem to be a significant problem, as there are usually multiple ways to express an idea in varying lengths. Also, with confidence-aware parallel decoding, it can usually avoid the scenario you mentioned, as focusing on decoding the highest confident tokens will generally avoid such scenarios with a well trained model.