Scaling inference

With the release of OpenAI's o1 and o3 models, it seems likely that we are now contending with a new scaling paradigm: spending more compute on model inference at run-time reliably improves model performance. As shown below, o1's AIME accuracy increases at a constant rate with the logarithm of test-time compute (OpenAI, 2024).

OpenAI's o3 model continues this trend with record-breaking performance, scoring:

• 2727 on Codeforces, which makes it the 175th best competitive programmer on Earth;
• 25% on FrontierMath, where "each problem demands hours of work from expert mathematicians";
• 88% on GPQA, where 70% represents PhD-level science knowledge;
• 88% on ARC-AGI, where the average Mechanical Turk human worker scores 75% on hard visual reasoning problems.

According to OpenAI, the bulk of model performance improvement in the o-series of models comes from increasing the length of chain-of-thought (and possibly further techniques like "tree-of-thought") and improving the chain-of-thought [...]

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Outline:

(00:05) Scaling inference

(02:45) AI safety implications

(03:58) AGI timelines

(04:50) Deployment overhang

(06:06) Chain-of-thought oversight

(07:40) AI security

(09:05) Interpretability

(10:01) More RL?

(11:27) Export controls

(11:54) Conclusion

The original text contained 2 footnotes which were omitted from this narration.

The original text contained 7 images which were described by AI.

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