"OpenAI's new o3 system - trained on the ARC-AGI-1 Public Training set - has scored a breakthrough 75.7% on the Semi-Private Evaluation set at our stated public leaderboard $10k compute limit.
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"OpenAI's new o3 system - trained on the ARC-AGI-1 Public Training set - has scored a breakthrough 75.7% on the Semi-Private Evaluation set at our stated public leaderboard $10k compute limit. A high-compute (172x) o3 configuration scored 87.5%.
This is a surprising and important step-function increase in AI capabilities, showing novel task adaptation ability never seen before in the GPT-family models. For context, ARC-AGI-1 took 4 years to go from 0% with GPT-3 in 2020 to 5% in 2024 with GPT-4o. All intuition about AI capabilities will need to get updated for o3.
The mission of ARC Prize goes beyond our first benchmark: to be a North Star towards AGI. And we're excited to be working with the OpenAI team and others next year to continue to design next-gen, enduring AGI benchmarks.
ARC-AGI-2 (same format - verified easy for humans, harder for AI) will launch alongside ARC Prize 2025. We're committed to running the Grand Prize competition until a high-efficiency, open-source solution scoring 85% is created."
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Miguel Afonso Caetanoreplied to Miguel Afonso Caetano last edited by
"For now, we can only speculate about the exact specifics of how o3 works. But o3's core mechanism appears to be natural language program search and execution within token space – at test time, the model searches over the space of possible Chains of Thought (CoTs) describing the steps required to solve the task, in a fashion perhaps not too dissimilar to AlphaZero-style Monte-Carlo tree search. In the case of o3, the search is presumably guided by some kind of evaluator model. To note, Demis Hassabis hinted back in a June 2023 interview that DeepMind had been researching this very idea – this line of work has been a long time coming.
So while single-generation LLMs struggle with novelty, o3 overcomes this by generating and executing its own programs, where the program itself (the CoT) becomes the artifact of knowledge recombination. Although this is not the only viable approach to test-time knowledge recombination (you could also do test-time training, or search in latent space), it represents the current state-of-the-art as per these new ARC-AGI numbers."
