[samsartor]

Well that is the whole trick. ML models ideally generalize from the training inputs to whatever new inputs show up during inference. For example, a vision model should recognize an image of a dog as a dog even if that exact image was not trained on. But that generalization always has limits. Usually score will decrease substantially the further "out-of-domain" the inputs are. So this model works fine when running a randomly generated dungeon it has never seen, but not when running a set of game rules it has never seen.