Naively, is there a case for using the average of the two?

EDIT: Someone on lesswrong linked a great report by Epoch which tries to answer exactly this.

With the release of openAI o1, I want to ask a question I've been wondering about for a few months.

Like the chinchilla paper, which estimated the optimal ratio of data to compute, are there any similar estimates for the optimal ratio of compute to spend on inference vs training?

In the release they show this chart:

The chart somewhat gets at what I want to know, but doesn't answer it completely. How much additional inference compute would I need a 1e25 o1-like model to perform as well as a one shotted 1e26?

Additionally, for some x number of queries, what is the optimal ratio of compute to spend on training versus inference? How does that change for different values of x?

Are there any public attempts at estimating this stuff? If so, where can I read about it?

Thanks for all of the hard work you put into developing and maintaining it! 

Thanks, this was quite informative. Would love to read the full report, but $1000 is a bit steep!

https://forum.effectivealtruism.org/posts/34pz6ni3muwPnenLS/why-so-many-racists-at-manifest

Without having read the letter yet, why do you find it questionable?

Good question, not sure how I get it into my email actually, I can't find it on the website either

edit: I think it's through the forecasting newsletter

I can highly recommend following Sentinel's weekly minutes, a weekly update from superforecasters on the likelihood of any events which plausibly could cause worldwide catastrophe.

Perhaps the weekly newsletter I look the most forward to at this point. Read previous issues here: 

https://sentinel-team.org/blog/

Jeff, your notes on NAO are fascinating to read! I have nothing to add other than that I hope you keep posting them