[wild_pointer]

I wonder how much of it is due to the model being familiar with the game or parts of it, be it due to training of the game itself, or reading/watching walkthroughs online.

[andrepd]

There was a well-publicised "Claude plays Pokémon" stream where Claude failed to complete Pokemon Blue in spectacular fashion, despite weeks of trying. I think only a very gullible person would assume that future LLMs didn't specifically bake this into their training, as they do for popular benchmarks or for penguins riding a bike.

[dwaltrip]

If they game the pelican benchmark, it’d be pretty obvious.

Just try other random, non-realistic things like “a giraffe walking a tightrope”, “a car sitting at a cafe eating a pizza”, etc.

If the results are dramatically different, then they gamed it. If they are similar in quality, then they probably didn’t.

[criley2]

While it is true that model makers are increasingly trying to game benchmarks, it's also true that benchmark-chasing is lowering model quality. GPT 5, 5.1 and 5.2 have been nearly universally panned by almost every class of user, despite being a benchmark monster. In fact, the more OpenAI tries to benchmark-max, the worse their models seem to get.

[ctoth]

> as they do for popular benchmarks or for penguins riding a bike.

Citation?