Thats not really equivalent, as image diffusion models are training on "real world" styles and topics, not a constrained game. Additionally, AlphaGo outputs are

Thats not really equivalent, as image diffusion models are training on "real world" styles and topics, not a constrained game. Additionally, AlphaGo outputs are quantifiably "flawless," whereas GenAI models have all sorts of artifacts and distortion in their output that can't possibly be reliably filtered, even with an army of humans to do it. My experience with ESRGAN is that it really does not like to train on its own output. It amplifies all sorts of wierd artifacts you can barely see I'd you go back and squint at the finetuning dataset. And there was a paper suggesting newer generative models are similar.