You want to know if a new model is actually better, which you won't know if they just added the specific example to the training set. It's like handing a dev on your team some failing test cases, and they keep just adding special cases to make the tests pass.

How many examples does OpenAI train on now that are just variants of counting the Rs in strawberry?

I guess they have a bunch of different wine glasses in their image set now, since that was a meme, but they still completely fail to draw an open book with the cover side up.

Being able to test future models without fear that your prompt has just been trained on an answer on HN, I assume.

To gauge how well the models "think" and what amount of slop they generate.

Keeping it secret because I don't want my answers trained into a model.

Think of it this way, FizzBuzz used to be a good test to weed out bad actors. It's simple enough that any first year programmer can do it and do it quickly. But now everybody knows to prep for FizzBuzz so you can't be sure if your candidate knows basic programming or just memorized a solution without understanding what it does.