2 ms·

wouldn't that imply some sort of "subconscious" reasoning ability that's hidden? and if that was the case why would we need cot in the first place?

HarHarVeryFunny 12d ago
A traditional Transformer is just a fixed-size stack of Transformer layers (typically ~100). Each input gets passed though those ~100 layers and results in one output token being generated. The limitation here is that 100 steps of computation is not enough to answer many questions, which is why COT is needed. You encourage the model (originally "think step by step") to break the problem down and generate more tokens along the way, which means more compute (and more reasoning steps) applied to the problem. What OpenAI have reportedly done with Astra is switch from a traditional Transformer to a "looped" one, where inputs are looped through some layers more than once, but with some limit, so now perhaps you get 200 steps of compute (layers) per token generated, rather than just 100. But, 200 steps is still not enough to answer any question, so you still need COT, but perhaps not such a long COT. If you allowed the Transformer to loop "as long as it wanted" before generating each token, like a person thinking before talking, then you wouldn't need an external COT ("thinking out loud"), because it would all be internal.
- dilyevsky 12d 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 12d ago
    Yes, 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.

HarHarVeryFunny 12d ago

A traditional Transformer is just a fixed-size stack of Transformer layers (typically ~100). Each input gets passed though those ~100 layers and results in one output token being generated. The limitation here is that 100 steps of computation is not enough to answer many questions, which is why COT is needed. You encourage the model (originally "think step by step") to break the problem down and generate more tokens along the way, which means more compute (and more reasoning steps) applied to the problem. What OpenAI have reportedly done with Astra is switch from a traditional Transformer to a "looped" one, where inputs are looped through some layers more than once, but with some limit, so now perhaps you get 200 steps of compute (layers) per token generated, rather than just 100. But, 200 steps is still not enough to answer any question, so you still need COT, but perhaps not such a long COT. If you allowed the Transformer to loop "as long as it wanted" before generating each token, like a person thinking before talking, then you wouldn't need an external COT ("thinking out loud"), because it would all be internal.

dilyevsky 12d 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 12d ago
  Yes, 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.