A priori, this makes sense: you don't need to train on humans to get a better understanding of the game tree. (See any number of other AIs that have learned to play games from scratch, given nothing but an optimization function.)
I don't think there is a theoretical upper limit on this kind of learning. If you do it sufficiently broadly, you will continuously improve your model over time. I suppose it depends to what extent you're willing to explicitly explore the game tree itself.