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At least in this scenario it cannot utilize CoT to enhance its non-aligned output, and most recent model improvements have been due to CoT... unclear how "smart" a llm can get without it, because they're the only way it can access persistent state.


Yes, it's not unlike human chain of thought - decide the outcome, and patch in some plausible reasoning after the fact.


Maybe there’s an angle there. Get a guess answer, then try to diffuse the reasoning. If it is too hard or the reasoning starts to look crappy, try again new guess. Maybe somehow train on what sort of guesses work out somehow, haha.


That's famously been found in, say, judgement calls, but I don't think it's how we solve a tricky calculus problem, or write code.




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