One thing about the reasoning is that models are trained to generate a chain of thoughts, but it doesn’t have to be correct, accurate, or reflect the underlying logic of the LLM. It’s the same problem we have with the output, it is something plausible, but not that reliable
Yes, both the output should be "milestones" of sorts, like lemmas and theorems in math. Important plateaus that serve as a launching pad to the next phase. Regurgitating every thought potentially degrades signal:noise ratio.
Actually, how does chain of thought work? Is the LLM actually creating the tokens and then re-reading them, or is the there still a full hidden state under the hood and then the UI just prints that projection?
Maybe, I wouldn’t know. I just find it fascinating that other people have such a radically different way of experiencing thought. No idea what it’s like.
What I'd like to see is "brain"-machine interface for LLMs that plugs things like calculator and other modules, directly into the neural network instead of accessing them through in-context tool calls.