That's what a few machine learning people I talked too thought would happen. There are lots of examples (self driving cars being the big one) in machine learning where training on individual examples isn't enough. We were actually hoping for our models to be as strong as the humans they're trained on so we are underperforming our target in that way.

I'm the main developer for Maia chess

Have you thought about trying a GAN or an actor-critic approach? Did you find it infeasible?

> Because of only predicting moves in isolation.

This actually may be the reason of higher ranking. What are the odds that a low ranked player will blunder a piece in a particular position? Quite low. But what are the odds that a low ranked player will blunder a piece in a game? They are quite high. So while this engine may predict most likely move, it can’t fake a likely game because it is too consistent.

I think GANs can be helpful to do something like this. One NN tries to make a man-like move and another one tries to guess whether the move was made by a human or the engine given the history of moves in a game.

I suppose the game at 1100 is really bad, such that it's mainly about avoiding obvious blunders, and not about having a sound long term strategy.