I believe it's something along these lines. The MTP head runs simultaneously and generates a probability list based on what it thinks the results will be, learned during training.

If n+1 = "Barack" then n+2 = "Obama" (confidence: 0.90) If n+1 = "The" then n+2 = "quick" (confidence: 0.45) If n+1 = "President" then n+2 = "Biden" (confidence: 0.75)

A threshold is set (say, as 90%) so that if the n+2 prediction is above that (as in the first example) it uses it without having to determine it with the main model. It's confident "enough".