Yeah. My point is essentially that I think they don't appear in the training text often enough, since it's trained on written, edited text, which don't often contain changes of opinion mid-answer, unlike spoken text, for example.
Perhaps if it could somehow be trained on a dataset of humans writing responses, including editing (e.g. removal of previously-written words), that might lead to better responses, since it can then edit the answer after reasoning it through (in OP's example).
Or, to simplify things, perhaps it should be allowed to emit a token meaning "actually, scrap all that, let me start over" when it detects a contradiction in its own response so far. (The model would still continue to see its own response including that token, but the UI could remove everything until the last instance of the token, if any.)
Or, again alternatively, if we want a response closer to spoken text, it could be trained on dialogues from humans that change their mind occasionally, and then verbalize that explicitly (e.g. actually emit "actually, scrap all that, let me start over").