My experience is mostly around either ensembles of weak learners that are bootstrapped to create a strong learner, or around boosted learners where you're training on residuals. With CoT you're sort of adding entropy to your answer, but you're also possibly getting a more thorough answer?

I'm curious though if these training methods are convergent or are models actually different; just like how in the stock market people think they're "diversified" but the truth is their exposure is likely much more risk correlated than one might think.

In certain situations, one right answer is better than a committee discussing the problem, but in others its sometimes nice to have some alternative methods of solving something. Fun project nonetheless.

My approach to using multiple models has been less about CoT but more about time to first token, and how you can use a small model to start interacting with the user while in parallel the more complex model is building a larger more complex thought. My work on this was primarily for voice backed interfaces before the voice models became quite a lot faster.