Thanks for the comment! I didn't get around to testing that, but that's one of the exact things I had in mind for my "Next Steps" #3 on training regimens that more reliably produce optimal, interpretable models.
Interesting, I'll definitely look into that! Sounds quite related.
Very true. If a token truly never appears in the training data, it wouldn't be trained / learned at all. Or similarly, if it's only seen like once or twice it ends up "undertrained" and the token frequency feature doesn't perform as well on it. The two least frequent tokens in the nanoGPT model are an excellent example of this. They appear like only once or twice and as a result don't get properly learned, and as a result end up being big outliers.
