Not to convergence, the graphs in the paper keep going up. Which across the analogy might explain some of the change from o1 to o3 (the graphs in the o1 post also keep going up), though new graders coded for additional verifiable problems are no doubt a large part of it as well.
o3-mini has the same knowledge cutoff date as 4o and o1 (late 2023)
It seems like o1-mini is its own thing, might even start with a base model that’s unrelated to GPT-4o-mini (it might be using its own specialized pretraining data mix). So a clue about o3-mini data doesn’t obviously transfer to o3.
if it used GPT-5 as a base model
The numbering in GPT-N series advances with roughly 100x in raw compute at a time. If original GPT-4 is 2e25 FLOPs, then a GPT-5 would need 2e27 FLOPs, and a 100K H100s training system (like the Microsoft/OpenAI system at the site near the Goodyear airport) can only get you 3e26 FLOPs or so (in BF16 in 3 months). The initial Stargate training system at Abilene site, after it gets 300K B200s, will be 7x stronger than that, so will be able to get 2e27 FLOPs. Thus I expect GPT-5 in 2026 if OpenAI keeps following the naming convention, while the new 100K H100s model this year will be GPT-4.5o or something like that.
Not to convergence, the graphs in the paper keep going up.
On page 10, when describing the training process for R1, they write: “We then apply RL training on the fine-tuned model until it achieves convergence on reasoning tasks.” I refer to this.
I basically agree with your analysis of GPT-5--which is worrying for short-term scaling, as I tried to argue.
they write: “We then apply RL training on the fine-tuned model until it achieves convergence on reasoning tasks.”
Ah, I failed to take a note of that when reading the paper. My takeaway was the opposite. In Figure 2 for R1-Zero, the first impression is convergence, both from saturation of the benchmark, and in the graph apparently leveling off. But if replotted in log-steps instead of linear steps, there isn’t even any leveling off for pass@1, despite near-saturation of the benchmark for cons@16: accuracy for pass@1 is 0.45 after 2K steps, 0.55 (+0.10) after 4K steps, then 0.67 (+0.12) after 8K steps, it just keeps going up by +0.10 every doubling in training steps. And the plots-that-don’t-level-off in the o1 post are in log-steps. Also, the average number of reasoning steps for R1-Zero in Figure 3 is a straight line that’s probably good for something if it goes further up. So I guess I might disagree with the authors even, in characterizing step 10K as “at convergence”, though your quote is about R1 rather than R1-Zero for which there are plots in the paper...
your analysis of GPT-5--which is worrying for short-term scaling
Well, I mostly argued about naming, not facts, though the recent news seem to be suggesting that the facts are a bit better than I expected only a month ago, namely 1 GW training systems might only get built in 2026 rather than in 2025, except possibly at Google. And as a result even Google might feel less pressure to actually get this done in 2025.
Not to convergence, the graphs in the paper keep going up. Which across the analogy might explain some of the change from o1 to o3 (the graphs in the o1 post also keep going up), though new graders coded for additional verifiable problems are no doubt a large part of it as well.
It seems like o1-mini is its own thing, might even start with a base model that’s unrelated to GPT-4o-mini (it might be using its own specialized pretraining data mix). So a clue about o3-mini data doesn’t obviously transfer to o3.
The numbering in GPT-N series advances with roughly 100x in raw compute at a time. If original GPT-4 is 2e25 FLOPs, then a GPT-5 would need 2e27 FLOPs, and a 100K H100s training system (like the Microsoft/OpenAI system at the site near the Goodyear airport) can only get you 3e26 FLOPs or so (in BF16 in 3 months). The initial Stargate training system at Abilene site, after it gets 300K B200s, will be 7x stronger than that, so will be able to get 2e27 FLOPs. Thus I expect GPT-5 in 2026 if OpenAI keeps following the naming convention, while the new 100K H100s model this year will be GPT-4.5o or something like that.
Thanks for your comments!
On page 10, when describing the training process for R1, they write: “We then apply RL training on the fine-tuned model until it achieves convergence on reasoning tasks.” I refer to this.
I basically agree with your analysis of GPT-5--which is worrying for short-term scaling, as I tried to argue.
Ah, I failed to take a note of that when reading the paper. My takeaway was the opposite. In Figure 2 for R1-Zero, the first impression is convergence, both from saturation of the benchmark, and in the graph apparently leveling off. But if replotted in log-steps instead of linear steps, there isn’t even any leveling off for pass@1, despite near-saturation of the benchmark for cons@16: accuracy for pass@1 is 0.45 after 2K steps, 0.55 (+0.10) after 4K steps, then 0.67 (+0.12) after 8K steps, it just keeps going up by +0.10 every doubling in training steps. And the plots-that-don’t-level-off in the o1 post are in log-steps. Also, the average number of reasoning steps for R1-Zero in Figure 3 is a straight line that’s probably good for something if it goes further up. So I guess I might disagree with the authors even, in characterizing step 10K as “at convergence”, though your quote is about R1 rather than R1-Zero for which there are plots in the paper...
Well, I mostly argued about naming, not facts, though the recent news seem to be suggesting that the facts are a bit better than I expected only a month ago, namely 1 GW training systems might only get built in 2026 rather than in 2025, except possibly at Google. And as a result even Google might feel less pressure to actually get this done in 2025.