Yeah, these benchmarks are bogus.

Every new model overfits to the latest overhyped benchmark.

Someone should take this to a logical extreme and train a tiny model that scores better on a specific benchmark.

All shared machine learning benchmarks are a little bit bogus, for a really “machine learning 101” reason: your test set only yields an unbiased performance metric if you agree to only use it once. But that just isn’t a realistic way to use a shared benchmark. Using them repeatedly is kind of the whole point.

But even an imperfect yardstick is better than no yardstick at all. You’ve just got to remember to maintain a healthy level of skepticism is all.

> Yeah, these benchmarks are bogus.

It's not just over-fitting to leading benchmarks, there's also too many degrees of freedom in how a model is tested (harness, etc). Until there's standardized documentation enabling independent replication, it's all just benchmarketing .

This has been done: https://arxiv.org/abs/2510.04871v1