In CoT, you give a prompt, then you have your chain of thought model (model 1) generate a bunch of tokens working on the problem. When some condition occurs (Either the model claiming it is done or you run out of thinking tokens (or some learned balance of the two), you stop running the Chain of Thought model. You then have another model, (model 2) take that chain of thought and present the answer/conclusion to the user. You can mix and match models between these two steps.
In their delivery as packaged products, most providers use the same model for model 1 and model 2, (as far as is public) or variants that have been fine-tuned together in such a way.
For models with public chain of thought, you can test this mixing and matching.
You can even staple chains of thoughts from multiple different generators together.
Moving fully to neuralese or a specialized encoding for mainline (not just secret), reasoning would mean that mixing and matching models would likely not work anymore, while currently it does.
I’m having trouble understanding your suggestion, especially the second paragraph. Could you spell it out a bit more?
In CoT, you give a prompt, then you have your chain of thought model (model 1) generate a bunch of tokens working on the problem. When some condition occurs (Either the model claiming it is done or you run out of thinking tokens (or some learned balance of the two), you stop running the Chain of Thought model. You then have another model, (model 2) take that chain of thought and present the answer/conclusion to the user. You can mix and match models between these two steps.
In their delivery as packaged products, most providers use the same model for model 1 and model 2, (as far as is public) or variants that have been fine-tuned together in such a way.
For models with public chain of thought, you can test this mixing and matching.
You can even staple chains of thoughts from multiple different generators together.
Moving fully to neuralese or a specialized encoding for mainline (not just secret), reasoning would mean that mixing and matching models would likely not work anymore, while currently it does.