Great. Yeah, I also expect that it is hard to get current models to work well on this. However, I will mention that the DeepSeekMath model does seem to outperform GPT-4 despite having only 7B parameters. So, it may be possible to create a +70B fine-tune that basically destroys GPT-4 at math. The issue is whether it generalizes to the kind of math we’d commonly see in alignment research.
Additionally, I expect at least a bit can be done with scaffolding, search, etc. I think the issue with many prompting methods atm is that they are specifically trying to get the model to arrive at solutions on their own. And what I mean by that is that they are starting from the frame of “how can we get LLMs to solve x math task on their own,” instead of “how do we augment the researcher’s ability to arrive at (better) proofs more efficiently using LLMs.” So, I think there’s room for product building that does not involve “can you solve this math question from scratch,” though I see the value in getting that to work as well.
Great. Yeah, I also expect that it is hard to get current models to work well on this. However, I will mention that the DeepSeekMath model does seem to outperform GPT-4 despite having only 7B parameters. So, it may be possible to create a +70B fine-tune that basically destroys GPT-4 at math. The issue is whether it generalizes to the kind of math we’d commonly see in alignment research.
Additionally, I expect at least a bit can be done with scaffolding, search, etc. I think the issue with many prompting methods atm is that they are specifically trying to get the model to arrive at solutions on their own. And what I mean by that is that they are starting from the frame of “how can we get LLMs to solve x math task on their own,” instead of “how do we augment the researcher’s ability to arrive at (better) proofs more efficiently using LLMs.” So, I think there’s room for product building that does not involve “can you solve this math question from scratch,” though I see the value in getting that to work as well.