o1 essentially takes longer to 'think' about questions before answering them, breaking down problems and checking its own answers. This approach highlights the potential benefits of reasoning models over sheer size.
Models like o1 demonstrate that scaling up training compute isn't the only way to improve a model's performance, suggesting alternative paths for AI development that don’t solely rely on massive resources.
Future AI systems may rely on small, easier-to-train 'reasoning cores' as opposed to training-intensive architectures, indicating a shift in the approach to building effective AI models.
It's a combination of bad science combined with policies that put the emphasis on not considering the inferencing capabilities, suggesting a need for a more nuanced regulatory framework.
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