@emollick
More evidence that o3 represents a big move forward, this time on ARC-AGI. https://t.co/Ccm2zER5Xp
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"text": "ARC-AGI o3 retest results are in!\n\nTakeaway: o3 (medium) is the industry leading AI reasoning system by large margin. 2X score and 1/20 cost compared to next leading chain-of-thought system as measured by ARC v1 semiprivate set scoring 57% for $1.5/task.\n\nOn v2, o3 (medium) scores only 3% for $2.5/task.\n\nWe could not get complete data for o3 (high) test due to repeat timeouts. Fewer than half of tasks returned any result exhausting >$50k test budget. We really tried!\n\nMy analysis below, focussing on o3:\n\nWhen we tested o3-preview in Dec 2024 I said “your intuition about AI capability will need to get updated”.\n\nSo my key question for released o3 — is it more like o1 (slightly better than pure LLM on novel tasks) or more like o3-preview (qualitative new capability to solve problems outside training data)?\n\nOur retest data suggests o3 (medium) has most of the qualitative new capability we saw from o3-preview, at a dramatically lower cost.\n\nWhile o3 (medium) accuracy is strictly lower than o3-preview (76% for $200/task), OpenAI did an extremely good job optimizing accuracy and cost for o3. You cannot buy o3’s level of AI reasoning capability anywhere else today.\n\n(We also know there is an upcoming o3-pro which I expect will be more akin to the higher performance and lower efficiency we saw with o3-preview.)\n\no3 (medium) is so good on ARC v1 for the cost, it’s hard to explain as a pure autoregressive CoT system like o1. The data suggests something more is going on.\n\nWhile o3 is definitely not doing massive, slow, expensive parallel sampling like o3-preview, there is evidence o3’s accuracy is more than just a function of model and thinking token count (\"time spent thinking\"). There is an additional X factor:\n\ntask_success = f(model, thinking_tokens, x_factor)\n\nSee charts below. The evidence supporting this is:\n\n1- o3 tokens/sec is consistent between low, medium and high modes, suggesting no major model change (unlike o3/o4-mini, o1-pro with big tokens/sec diffs)\n\n2- o3 (high) v1 success is strictly better than medium than low, given an arbitrary thinking tokens cutoff\n\nI can’t say for certain what the X factor is. There are many possible explanations. It could be a consistent parallel CoT step sampling like o3-preview. It could be a varying “grader” model. It could be an MoE with different heads activated. Or something else entirely.\n\nMy informed speculation is o3 low/medium/high scales reasoning along both CoT width and depth — which enables a strong recombination of knowledge from the base model train.\n\nWe know this method beats v1 based on o3-preview, so it stands to reason o3 re-uses some of the core ideas that work.\n\nA few conclusions:\n\n1- Release o3, especially medium and high mode, is more like o3-preview than o1, capability wise.\n\n2- You should default to o3 (high) timeouts notwithstanding. Or medium if you experience timeouts. There is no compelling reason to use low if you care about accuracy.\n\n3- ARC v1 is a surprisingly useful tool for acquiring insight on frontier AI systems. v1 tasks still “stress” these systems which reveals efficiency and capability limits.\n\n4- ARC v2 has a long way to go still, even with the great reasoning efficiency of o3. New ideas are still needed!",
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