The "Deep Symbolic Regression" paper reports very poor generalisation results that break off after a small n (where n is the number of tokens in the predicted sequence). It works some of the time for n = 1 (predicts the next token) but accuracy drops off for n = 10. No results are reported for N > 10 as far as I can tell in the "Out of Domain Generalization" section (which is the meat and potatoes of the "generalization" claim).

tl;dr they can sometimes generalise to the next 1 to 10 tokens (digits or operators), but no more.

This kind of short-term "generalisation" on OOD data is standard in neural nets trying to approximate symbolic regressions or things like grammars etc as far as I know.

I do like they use 'Out of Domain" rather than "Out of Distribution" as a target though. That makes more sense.