This is cool! I think I'm updating toward the logistic fit not mattering. The question I have now is: what would it have taken on this underlying data for the log-linear trend not to hold. My guess is models not making progress for months, and staying at similar aggregate accuracy (with success rates staying roughly inversely correlated with task length).
The mean estimate of 50% success horizon length (headline number METR reports) went from ~1 to ~4 hours. The progress within the hour subranges is difficult to draw much information from, given the low number of data points, and distribution biases in topics. This is the precise claim of the new post I made, and linked :)
Thanks for checking this. Log-linear isn't that different from logistic in how it would affect the downstream prediction. Could you (someone at METR) update the public all-results file on GitHub so we can play around with this data?
I am particularly curious to know what would happen if we took the 50% horizon as the startpoint of the first bar the model drops below 50% accuracy. This increases uncertainty, but it would be interesting to see what trend comes out, and how model rankings change (is opus 4.5 a big update?).
I do expect it would still be an exponential trend, and agree with you that the underlying data distribution (specifically the topics aligning exactly with frontier lab priorities) is the more risky confounder. Although one could argue for choosing to do it this way, it just reduces chances of the horizon length being relevant outside the model's strongest areas.
thats an interesting point. If I kept adding points to the right, i.e. longer and longer tasks which I know the model would fail on, it would keep making the line flatter? That kind of makes me wonder, once again, if its even a good idea to try and fit a line here...
1Thanks, I should've done that myself instead of lazily mentioning what it "looked like". R^2=0.51 is still a lot lower than the initial 0.83. Though same as before, I am not fully sure what this implies for the logistic model chosen, and downstream conclusions.
1https://www.lesswrong.com/posts/2RwDgMXo6nh42egoC/how-to-game-the-metr-plot
Claude's performance is low on the 2-4 hour range, which mostly consists of cybersecurity tasks, potentially dual-use for safety. In general, training on cybersecurity CTFs and ML code would increase "horizon length" on the METR plot, which only has 14 samples in the relevant (1 - 4hr) range where progress happened in 2025.
Thanks these are some great ideas. another thing you guys might want to look into is shifting away from mcqs towards answer matching evaluations: https://www.lesswrong.com/posts/Qss7pWyPwCaxa3CvG/new-paper-it-is-time-to-move-on-from-mcqs-for-llm
Yes, that is a good takeaway!
That's quite possible. I'm not sure how much that plays out with reinforcement learning training though.