One thing I’d be much more excited about seeing rather than “quantifying post-training variables and their effects” (but which I’m not planning to pursue) would be to take an old model and then to try to map post-training enhancements discovered over time and see how the maximum elicitable capabilities change.
I’m worried that quantifying post-training variables directly has significant capabilities externalities and that there’s no obvious limit to how far post-training can be pushed.
I’d also be excited about projects aiming to do this.
One advantage that quantifying post-training variables on frontier models has over this idea is that you also get a better sense of what the upper bound of performance on some eval looks like, as well as some information about the returns from investing in post-training enhancements. I think if this were done responsibly on some well-chosen evals, it’d be helpful information to have. (Though my colleagues may disagree.)
If people outside of frontier labs were working on this, I’d be surprised if it significantly accelerated capabilities, though I can imagine it still making sense to keep the methodology private.
One thing I’d be much more excited about seeing rather than “quantifying post-training variables and their effects” (but which I’m not planning to pursue) would be to take an old model and then to try to map post-training enhancements discovered over time and see how the maximum elicitable capabilities change.
I’m worried that quantifying post-training variables directly has significant capabilities externalities and that there’s no obvious limit to how far post-training can be pushed.
I’d also be excited about projects aiming to do this.
One advantage that quantifying post-training variables on frontier models has over this idea is that you also get a better sense of what the upper bound of performance on some eval looks like, as well as some information about the returns from investing in post-training enhancements. I think if this were done responsibly on some well-chosen evals, it’d be helpful information to have. (Though my colleagues may disagree.)
If people outside of frontier labs were working on this, I’d be surprised if it significantly accelerated capabilities, though I can imagine it still making sense to keep the methodology private.