moozooh
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moozooh has not written any posts yet.
I agree with this and would like to add that scaling along the inference-time axis seems to be more likely to rapidly push performance in certain closed-domain reasoning tasks far beyond human intelligence capabilities (likely already this year!) which will serve as a very convincing show of safety to many people and will lead to wide adoption of such models for intellectual task automation. But without the various forms of experiential and common-sense reasoning humans have, there's no telling where and how such a "superhuman" model may catastrophically mess up simply because it doesn't understand a lot of things any human being takes for granted. Given the current state of AI development, this strikes me as literally the shortest path to a paperclip maximizer. Well, maybe not that catastrophic, but hey, you never know.
In terms of how immediately it accelerates certain adoption-related risks, I don't think this bodes particularly well. I would prefer a more evenly spread cognitive capability.
I don't think o3 is a bigger model if we're talking just raw parameter count. I am reasonably sure that both o1, o3, and the future o-series models for the time being are all based on 4o and scale its fundamental capabilities and knowledge. I also think that 4o itself was created specifically for the test-time compute scaffolding because the previous GPT-4 versions were far too bulky. You might've noticed that pretty much the entire of 2024 for the top labs was about distillation and miniaturization where the best-performing models were all significantly smaller than the best-performing models up through the winter of 2023/2024.
In my understanding, the cost increase comes from the... (read more)
Accuracy being halved going from 5.1 to 5.2 suggests one of the two things:
1) the new model shows dramatic regression on data retrieval which cannot possibly be the desired outcome for a successor, and I'm sure it would be noticed immediately on internal tests and benchmarks, etc.—we'd most likely see this manifest in real-world usage as well;
2) the new model refuses to guess much more often when it isn't too sure (being more cautious about answering wrong), which is a desired outcome meant to reduce hallucinations and slop. I'm betting this is exactly what we're looking at, and your Sonnet graph also suggests the same.
So if your methodology counts refusal as lowering... (read more)