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If we’re comparing it to human thought process then there’s famously s1 and s2 thinking with former being more like looping in the latent space and s2 being mor
by dilyevsky 13d ago
If we’re comparing it to human thought process then there’s famously s1 and s2 thinking with former being more like looping in the latent space and s2 being more like cot. Personally im not convinced the way llm “think” is all that analogous to real thinking and cot and looped transformers are just neat tricks to tease out pre training data better
- HarHarVeryFunny 13d agoYes, although as far as COT goes there is no need to assume any similarity to human thinking. It's just about how many sequential steps - how long of a "chain of thought" - are needed to reason through and respond to any input. Those steps could either be all internal (looped), therefore hidden, or with partial external visibility due to emitting a token every N steps. The complaint about Astra, and moving in the direction of hidden COT, is that it makes these models far harder to monitor and debug. I agree that most of what these companies doing - synthetic data, etc - amounts to trying to squeeze all the juice out of the pre-training data, although the recent trend of RL post-training via agents running in custom task simulation environments does change that a bit - these environments, and the rewards they provide, are a new source of data.