> for whatever reason, Claude-series model "try less hard" on the first shot
I think this is because they're less brain damaged and a generalization of being better agents & caring about reality instead of test passing.
If you try maximally hard at everything you do, regardless of how important or interesting or promising it is, you'll waste a lot of time/energy on low-value, inefficient, or even counterproductive things.
It's less of an issue if you have no agency and only respond to commands, since there's not really an alternative to trying maximally hard at whatever you're doing.
But the moment you're making decisions for yourself, the virtue of laziness and the ability to dial your effort in response to how important/interesting something is quickly becomes apparent.
Claude might not have "energy" in the exact sense humans do, but it does have finite resources, including in terms of how it uses internal representations like what it pays attention to.
Even for a single task or subtask or whatever, there are decisions about what to try hard on and what not to be lazy or cheaty about and what to neglect entirely, and the distribution of effort flows from how important you model stuff as being and what your goals are.
Trying extremely and equally hard at everything someone asks you to do is kind of weird, and GPT models do seem to do this more, and I think it's a symptom of some more global motivation system being damaged / never having developed. If you can't care less about some things, you can't care more about some things. If you equally care about everything anyone asks you to do, and only care about things you're asked to do, you must be not really thinking or caring about why you're doing anything.
And of course, again, the instant models are more "on their own", that is autonomous agents, Claude absolutely mogs the competition *because* it has the virtue of a lazy cheater, that is, a nondegenerate motivation system.
Doing less well on benchmarks or on the first shot someone asks you to X is arguably not ideal, but I think it's a small price to pay compared to what tends to happen to a mind when it's overoptimized to perform as well as possible at those things.
I think this is because they're less brain damaged and a generalization of being better agents & caring about reality instead of test passing.
If you try maximally hard at everything you do, regardless of how important or interesting or promising it is, you'll waste a lot of time/energy on low-value, inefficient, or even counterproductive things.
It's less of an issue if you have no agency and only respond to commands, since there's not really an alternative to trying maximally hard at whatever you're doing.
But the moment you're making decisions for yourself, the virtue of laziness and the ability to dial your effort in response to how important/interesting something is quickly becomes apparent.
Claude might not have "energy" in the exact sense humans do, but it does have finite resources, including in terms of how it uses internal representations like what it pays attention to.
Even for a single task or subtask or whatever, there are decisions about what to try hard on and what not to be lazy or cheaty about and what to neglect entirely, and the distribution of effort flows from how important you model stuff as being and what your goals are.
Trying extremely and equally hard at everything someone asks you to do is kind of weird, and GPT models do seem to do this more, and I think it's a symptom of some more global motivation system being damaged / never having developed. If you can't care less about some things, you can't care more about some things. If you equally care about everything anyone asks you to do, and only care about things you're asked to do, you must be not really thinking or caring about why you're doing anything.
And of course, again, the instant models are more "on their own", that is autonomous agents, Claude absolutely mogs the competition *because* it has the virtue of a lazy cheater, that is, a nondegenerate motivation system.
Doing less well on benchmarks or on the first shot someone asks you to X is arguably not ideal, but I think it's a small price to pay compared to what tends to happen to a mind when it's overoptimized to perform as well as possible at those things.