Re: Neuralese not winning, I think during practical inference, you’d have similar-sized KV caches, so the memory usage is basically a wash (although storing the tokens when you’re not running would be much smaller).
But my understanding is that neuralese hasn’t won because it’s too hard to train. CoT works by training a base model to produce ~all kinds of human-like text, and then RL can extract human-like text that’s useful for reasoning. For neuralese, you have to train the reasoning from scratch, without teacher forcing, and getting that to work is (for now) too hard and not as effective as text CoT.
Thanks for the kind words, and for the correction! You’re right that the training bottleneck is the key issue, not inference costs. I’ve updated the article to reflect this + added a correction footnote. The section now explains how teacher forcing enables parallelization for text but breaks down for Neuralese. Appreciate you taking the time to flag it.
Re: Neuralese not winning, I think during practical inference, you’d have similar-sized KV caches, so the memory usage is basically a wash (although storing the tokens when you’re not running would be much smaller).
But my understanding is that neuralese hasn’t won because it’s too hard to train. CoT works by training a base model to produce ~all kinds of human-like text, and then RL can extract human-like text that’s useful for reasoning. For neuralese, you have to train the reasoning from scratch, without teacher forcing, and getting that to work is (for now) too hard and not as effective as text CoT.
Great article though!
Thanks for the kind words, and for the correction! You’re right that the training bottleneck is the key issue, not inference costs. I’ve updated the article to reflect this + added a correction footnote. The section now explains how teacher forcing enables parallelization for text but breaks down for Neuralese. Appreciate you taking the time to flag it.