I strongly expect that if you want to make progress on math at the current margin, you would want more math environments, not other environments. And e.g. I think Claude’s somewhat worse performance on math is because Anthropic didn’t prioritize it the way GDM and OAI did.
To be clear, this is also my belief!
I am not saying we have pushed capabilities on our training distributions so far that literally the best way to train them is to train them on other unrelated tasks. But also, if you just went totally hard on math, you would run into overfitting issues and would get better performance if you diversify the training distribution.
Performance variation within a generation is dependent on training distribution, performance between model generations tends to follow broad capability benefits across many tasks (with a systematic bias towards stuff that is easier to generate reward for, ever since we switched towards lots of RL training).
To be clear, this is also my belief!
I am not saying we have pushed capabilities on our training distributions so far that literally the best way to train them is to train them on other unrelated tasks. But also, if you just went totally hard on math, you would run into overfitting issues and would get better performance if you diversify the training distribution.
Performance variation within a generation is dependent on training distribution, performance between model generations tends to follow broad capability benefits across many tasks (with a systematic bias towards stuff that is easier to generate reward for, ever since we switched towards lots of RL training).
Not sure whether that changes your answer.
No, I did expect you had the same belief on the math thing. (Otherwise I wouldn’t have said “kinda hard to adjudicate” I’d have said “lc is right”.)
It just seemed like something that you might not have been fully incorporating into this discussion even though you believed it.