The discussion on filler tokens is interesting, but is it not just the case that these filler tokens end up being essentially substituted stand-ins for words we understand with all the same relationships encoded in the model and attention? i.e. is it not the case they just "read weird"?

In one of the articles on this topic they state: > To further show that trace accuracy is only loosely connected to solution accuracy, we then train models on noisy, corrupted traces which have no relation to the specific problem each is paired with, and find that not only does performance remain largely consistent with models trained on correct data, but in some cases can improve upon it and generalize more robustly on out-of-distribution tasks

which actually maps somewhat to regularisation techniques in image processing where you might add noise to an image or drop data to make the model more robust to changes.