I’m pretty sure “benchmaxxing” as defined is a standard post-training procedure for ALL models.
One can argue though some models are post-trained heavily for specific benchmarks and less for other datasets. So they are weaker on out of distribution tasks.
One can argue though some models are post-trained heavily for specific benchmarks and less for other datasets. So they are weaker on out of distribution tasks.