2 ms·

i wonder if this is a result of the GPT part (i.e. the distribution of 'next tokens' is such that it tends to be logically coherent), or the RLHF part (i.e. it

• nullc 4y ago
  The just internet trained models don't think out loud, so I assume it was RLHFed conditioned to that behavior since it makes it able to solve much more complicated questions, though it's sometimes irritating to read.
• mirekrusin 4y ago
  Both and none at the same time. Both contribute to overall performance. None are directly related to solving it in general. You can't solve it in general because you can't have answer to arbitrarily complex question with a single pass on limited data. However I do think that current methods are extremely inefficient. There is a lot of nonsense, noise and duplication in training data. It can be compressed to be orders of magnitude smaller. Imagine having dense, close to optimal way to kick off learning with logic, reasoning, things like set theory, category theory etc. so it is later reused when learning ie. programming language - where learning programming language is also efficient, based on cleaned up specification, standard library learned from code of standard library itself, possibly access to compiler/interpreter for feedback. I mean quality of data slider pushed to the limit. But this single pass, void of inner dialogue approach will always require articulating steps, no matter how good it is quality wise.