[rkomorn]

I'm certainly not challenging anything you're writing, because I only have a very distant understanding of deep learning, but I do find the question interesting.

Isn't there a bit of a defining line between something like tic-tac-toe that has a finite (and pretty limited for a computer) set of possible combinations where it seems like you shouldn't need a training set that is larger than said set of possible combinations, and something more open-ended where the impact of the size of your training set mainly impacts accuracy?

[dpoloncsak]

Assuming you don't account for reflections, rotations, and 'unreachable' gamestates where a player wins and you continue to mark boxes.

It's just 3^9, right? 9 boxes, either X,O, or blank? We're only at 19,683 game states and would trim down from here if we account for the cases above.

[rkomorn]

Exactly, but then we may as well say "don't solve this with an LLM" which sort of kills the conversation altogether and that's not my goal. :)

[dpoloncsak]

Oh, im sorry! I was just trying to give a quick perspective of how small that tic-tac-toe data-set actually is. Not suggest against the idea!

[rkomorn]

Oh no worries at all. :)