Chain of Continuous Thought

I came across an interesting reasoning approach in the paper Training Large Language Models to Reason in a Continuous Latent Space. It introduces something called Coconut, short for Chain of Continuous Thought, which flips the usual way large language models reason. Typically, these models work step by step, turning each part of their thought process into words. While that seems logical, it’s not always efficient or capable of handling the really complex stuff.

Coconut takes a different route by skipping the words altogether during reasoning. Instead, it uses what they call a “continuous thought,” essentially storing reasoning in a high-dimensional hidden state. The model doesn’t stop to spell things out for itself but instead jumps to the next step, embedding the reasoning directly. I think it’s like the difference between explaining every move in chess and just playing with an intuitive understanding of the game.

The results are pretty interesting. Coconut lets the model explore multiple possible solutions at once, making it much better at problems that require backtracking or trying different approaches. It’s also faster since it doesn’t need to churn out tokens to explain every intermediate step. This feels like a glimpse of the future-models that don’t just talk to us in words but actually think beyond them. It’s not perfect, though. What happens when this reasoning becomes too abstract to interpret? And how do we ensure it doesn’t latch onto the wrong ideas? These are the questions I’m now mulling over, and honestly, they’re just as cool to think about as the innovation itself.