d(expected gain)/d(opportunity cost) = 0
==>
expected gain \propto opportunity cost
It is the case that most metrics are logarithmic: it takes just as much effort to decrease one bit of error as the next bit. So log(score) \propto (opportunity cost) \propto expected gain
Thus, for them to be agnostic, you should filter candidates proportional to their log-score on the metric (where 0 is a perfect score). Because generally applicable skills are generally applicable, they will still benefit from improving those, they just no longer benefit from adversarial optimization, unless your score function looks very similar to others who have not adopted this filtering process. max nats = max entropy + energy / temperature
Why might energy correspond to bits or nats? Imagine your goal is to play as many interesting games of chess as possible in a tournament. This implies you have to keep winning. If you look at the RL environment from the right perspective, you can turn it into optimizing bits or nats.
https://si.inc/posts/hertz-dev/
It's only 8.5B and doesn't sound like it's quite conversational.