I think this is clearly a case of over fitting and failure to generalize, which are really well understood concepts. We don't have to philosophize about what pattern matching really means.

In the Physics of Language Models[1] they argue that you must augment your training data by changing sentences and such, in order for the model to be able to learn the knowledge. As I understand their argument, language models don't have a built-in way to detect what is important information and what is not, unlike us. Thus the training data must aid it by presenting important information in many different ways.

Doesn't seem unreasonable that the same holds in a gaming setting, that one should train on many variations of each level. Change the lengths of halls connecting rooms, change the appearance of each room, change power-up locations etc, and maybe even remove passages connecting rooms.

[1]: https://physics.allen-zhu.com/part-3-knowledge/part-3-1

In other words, they learn the game, not how to play games.

I kinda think I'm more or less the same...OK maybe we have different definitions of "pattern matching".

Where do you draw the line between pattern matching and reasoning about world models?

A lot of intelligence is just pattern matching and being quick about it.