These glitch tokens are all near the centroid of the token embedding space. That means that the model cannot really differentiate between these tokens and the others equally near the center of the embedding space, and therefore when asked to 'repeat' them, gets the wrong one.

That happened because the tokens were on the internet many millions of times (the davidjl user has 163,000 posts on reddit simply counting increasing numbers), yet the tokens themselves were never hard to predict (and therefore while training, the gradients became nearly zero, and the embedding vectors decayed to zero, which some optimizers will do when normalizing weights).