Curious if a harness that helped preserve signals in some history log would change the outcome.
Also curious if different goal prompts would have changed the outcome. Not a bunch of prompt engineering; small diffs like "consider novel solutions, keep track of weak signals".
IMO they allocated quite a bit of GPU time to the same goal prompt.
I'm also seeing notes like on Opus 5 saying it was a run with a older serial version of program.md, so the graphs aren't complete apples-to-apples comparisons?
Edit: the blog seems to address these https://www.primeintellect.ai/blog/measuring-autonomous-rese...
Uh.. okay.. but whats a run… read blog
“We want to measure how well frontier models can conduct research….””we ran 153 autonomous runs on the nanoGPT optimizer speedrun across”
Okay but what is a optimiser run and what connection does it have to being good at research?
“For comparison, Anthropic's internal automated AI R&D evaluation optimizes a model on a CPU node,”
So I should go look what Anthropic was doing to understand?
Why not just explain what it means in their blog..
https://www.primeintellect.ai/blog/measuring-autonomous-rese...
Basically they do 8 runs trying to optimize to under 3.28 loss in the fewest training steps possible under time/token constraint. I dunno why 18 * 8 != 153 (it's 144)
- "A frozen verify.py accepts the claim" (what does it mean to freeze a python script?)
- "which trains the recipe eight times on fixed seeds it can't touch" (what does it mean to not be able to touch a seed)
- "One other detail is that we gave an estimation of the speedrun noise in program.md that was slightly too large. 62 out of ~100 runs measured it themselves instead of trusting our number" (What does it mean for a "run" to "distrust" a noise measurement)
- "One important disclaimer is that our benchmark has a lot of variance" (Actually this one makes sense, but congratulations for burying the lede that your entire article is bogus.)
- "Almost every model finds the same winning ideas. What separates the best traces is what an experiment leaves behind" (This implies the the graph would show every model finding a plateau in whatever metric the experiment is measuring, but I don't see every model scoring the same in the graph)
They gave 18 frontier models the task of “researching” how to improve a lab-rat nano model’s training. Stopping when it met a quality goal of a target loss rate. During each autonomous research session, the AI repeatedly tried changes, tested them, and used the results to decide what to try next. They repeated the whole research session many times with different seeds to average out variance.
"We were again surprised by the lack of novelty. The models clearly understand the objects they manipulate at a deep level, and yet very few genuinely new ideas emerge..."
See you next year, guys?
The former will perform well in these long horizon benchmarks. The latter won’t.
Does anyone know the ratios of pretrain, posttrain supervised as well as reinforcement learning? (I should probably even distinguish between RLHF and RLVR).
I assume the latter is the main reason for the power of modern models. Is it possible to turn the results of a gym session into trading data?
(Sorry for moving in off topic regions, but I'm interested in that for a long time.)
Interesting times ahead.
Such a cheap model, and Sonnet levels of performance.
I've had a lot of success with dsv4-flash on these type of tasks, where it's easy to set a threshold for the task, and just loop it until that threshold is reached.
oAI's Luna play is really good. They've slashed the prices, the model is somewhat capable, and you can use it both for these kinds of long horizon tasks, or you can hand-hold a bit and get extremely cheap results out of it. And they get to keep devs in their own ecosystem.
I'm a co-author on a recent blog post from METR about the NanoGPT speed-run here [1]. I think it'd be of interest to anyone who enjoyed the original post. Appreciate the good beefy runs and spend here, it's a (from my experience) not super easy to do!
(Also: just to label this comment clearly: it's written hastily from a car, and based on lighter skim of the original blog post [2] than would be ideal. Please correct any mistakes or misinterpretations I have here!)
A few callouts:
1. If I understand the experiment correctly, they start the models at the original baseline. If this is true, I have some worries about contamination. Appendix C [3] has some notes on likely contamination we observed in recent models. This makes interpretation a bit harder.
2. If you look at the token scaling plots in the original post: not all models are hitting a performance plateau. This is an important point: we shouldn't treat these results as a full upper-bound on capabilities, but rather some bound on model performance @ cost (assuming good scaffolding, etc).
3. Our post is mostly about how to _interpret_ the results given here. Quoting from our post: "If we can estimate performance as a function of cost for both humans and agents, we can measure the “expenditure horizon” as the point at which those curves cross: the budget at which humans become more cost-effective than AIs. "
Feedback appreciated. I think you can see expenditure horizon as a sibling methodology (that is much less validated) to METR's time horizon work [4] - roughly, instead of baselining against the time it takes humans to complete tasks, you baseline against cost. This may be better suited to some types of problems similar to NanoGPT.
[1] https://metr.org/blog/2026-07-21-expenditure-horizon/ (Most of this work was my coauthors listed on the post, not me. I'll claim credit for any mistakes though :) ).
[2] https://www.primeintellect.ai/blog/measuring-autonomous-rese...
[3] https://metr.org/blog/2026-07-21-expenditure-horizon/#append...
[4]https://metr.org/time-horizons/
(Edit: METR is hiring. Email is in bio if you're interested in helping AI companies and wider society understand the capabilities and risks of AI.)
The graphs show the "best validated result" for each model. I wonder how much variation there is between runs for a model?
I wrote a quick review of Grok 4.6 here: https://taonexus.com/publicfiles/aug2026/grok-4-6-review/
Lol.
1. https://github.com/KellerJordan/modded-nanogpt#world-record-...
I just ran it the last couple days extensively to verify my data training pipeline I'm building for my gonano SIMD port.
Given that the speed records and the runs are sponsored by the same company I was confused a bit.