o3 gets 25% on FrontierMath, smashing the previous SoTA. (These are really hard math problems.[1]) Wow. (The dark blue bar, about 7%, is presumably one-attempt and most comparable to the old SoTA; unfortunately OpenAI didn't say what the light blue bar is, but I think it doesn't really matter and the 25% is for real.[2])
o3 also is easily SoTA on SWE-bench Verified and Codeforces.
It's also easily SoTA on ARC-AGI, after doing RL on the public ARC-AGI problems[3] + when spending $4,000 per task on inference (!).[4] (And at less inference cost.)
OpenAI says "We're offering safety and security researchers early access to our next frontier models"; yay.
o3-mini will be able to use a low, medium, or high amount of inference compute, depending on the task and the user's preferences. o3-mini (medium) outperforms o1 (at least on Codeforces and the 2024 AIME) with less inference cost.
Update: most of them are not as hard as I thought:
There are 3 tiers of difficulty within FrontierMath: 25% T1 = IMO/undergrad style problems, 50% T2 = grad/qualifying exam style [problems], 25% T3 = early researcher problems.
My guess is it's consensus@128 or something (i.e. write 128 answers and submit the most common one). Even if it's pass@n (i.e. submit n tries) rather than consensus@n, that's likely reasonable because I heard FrontierMath is designed to have easier-to-verify numerical-ish answers.
Thank you so much for your research! I would have never found these statements.
I'm still quite suspicious. Why would they be "including a (subset of) the public training set"? Is it accidental data contamination? They don't say so. Do they think simply including some questions and answers without reinforcement learning or reasoning would help the model solve other such questions? That's possible but not very likely.
Were they "including a (subset of) the public training set" in o3's base training data? Or in o3's reinforcement learning problem/answer sets?
Altman never said "we didn't go do specific work [targeting ARC-AGI]; this is just the general effort."
Instead he said,
Worth mentioning that we didn't, we target and we think it's an awesome benchmark but we didn't go do specific [inaudible]--you know, the general rule, but yeah really appreciate the partnership this was a fun one to do.
The gist I get is that he admits to targeting it but that OpenAI targets all kinds of problem/answer sets for reinforcement learning, not just ARC's public training set. It felt like he didn't want to talk about this too much, from the way he interrupted himself and changed the topic without clarifying what he meant.
The other sources do sort of imply no reinforcement learning. I'll wait to see if they make a clearer denial of reinforcement learning, rather than a "nondenial denial" which can be reinterpreted as "we didn't fine-tune o3 in the sense we didn't use a separate derivative of o3 (that's fine-tuned for just the test) to take the test."
My guess is o3 is tuned using the training set, since François Chollet (developer of ARC) somehow decided to label o3 as "tuned" and OpenAI isn't racing to correct this.
See livestream, site, OpenAI thread, Nat McAleese thread.
OpenAI announced (but isn't yet releasing) o3 and o3-mini (skipping o2 because of telecom company O2's trademark). "We plan to deploy these models early next year." "o3 is powered by further scaling up RL beyond o1"; I don't know whether it's a new base model.
o3 gets 25% on FrontierMath, smashing the previous SoTA. (These are really hard math problems.[1]) Wow. (The dark blue bar, about 7%, is presumably one-attempt and most comparable to the old SoTA; unfortunately OpenAI didn't say what the light blue bar is, but I think it doesn't really matter and the 25% is for real.[2])
o3 also is easily SoTA on SWE-bench Verified and Codeforces.
It's also easily SoTA on ARC-AGI,
after doing RL on the public ARC-AGI problems[3] + when spending $4,000 per task on inference (!).[4] (And at less inference cost.)ARC Prize says:
OpenAI has a "new alignment strategy." (Just about the "modern LLMs still comply with malicious prompts, overrefuse benign queries, and fall victim to jailbreak attacks" problem.) It looks like RLAIF/Constitutional AI. See Lawrence Chan's thread.[5]
OpenAI says "We're offering safety and security researchers early access to our next frontier models"; yay.
o3-mini will be able to use a low, medium, or high amount of inference compute, depending on the task and the user's preferences. o3-mini (medium) outperforms o1 (at least on Codeforces and the 2024 AIME) with less inference cost.
GPQA Diamond:
Update: most of them are not as hard as I thought:
My guess is it's consensus@128 or something (i.e. write 128 answers and submit the most common one). Even if it's pass@n (i.e. submit n tries) rather than consensus@n, that's likely reasonable because I heard FrontierMath is designed to have easier-to-verify numerical-ish answers.
Update: it's not pass@n.
Correction: no RL! See comment.
Correction to correction: nevermind, I'm confused.
It's not clear how they can leverage so much inference compute; they must be doing more than consensus@n. See Vladimir_Nesov's comment.
Update: see also disagreement from one of the authors.