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).

The image shows two scatter plots comparing "o1 AIME accuracy" during training and at test time. Both charts have "pass@1 accuracy" on the y-axis and compute (log scale) on the x-axis. The dots indicate increasing accuracy with more compute time.

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 (CoT) process with reinforcement learning. Running o3 at maximum performance is currently very expensive, with single ARC-AGI tasks costing ~$3k, but inference costs are falling by ~10x/year!

A recent analysis by Epoch AI indicated that frontier labs will probably spend similar resources on model training and inference.^[1] Therefore, unless we are approaching hard limits on inference scaling, I would bet that frontier labs will continue to pour resources into optimizing model inference and costs will continue to fall. In general, I expect that the inference scaling paradigm is probably here to stay and will be a crucial consideration for AGI safety.

AI safety implications

So what are the implications of an inference scaling paradigm for AI safety? In brief I think:

AGI timelines are largely unchanged, but might be a year closer.
There will probably be less of a deployment overhang for frontier models, as they will cost ~1000x more to deploy than expected, which reduces near-term risks from speed or collective superintelligence.
Chain-of-thought oversight is probably more useful, conditional on banning non-language CoT, and this is great for AI safety.
Smaller models that are more expensive to run are easier to steal, but harder to do anything with unless you are very wealthy, reducing the unilateralist's curse.
Scaling interpretability might be easier or harder; I'm not sure.
Mo

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Inference Scaling