Latent CoT: The Future of AI Reasoning?

In the evolving world of artificial intelligence, the concept of continuous chain-of-thought (CoT) reasoning is creating buzz. The idea is simple yet powerful: by operating in continuous space, AI models can potentially juggle multiple solutions at once through superposition. But is this happening in reality?

Exploring Latent CoT Models

Researchers are diving into three different approaches to understand if AI models truly tap into superposition in their reasoning. These approaches include a training-free model, where latent thoughts are crafted as convex combinations of token embeddings, a fine-tuned model adapting a base AI to generate latent thoughts, and a from-scratch model trained entirely with latent thoughts.

Upon analysis using methods like Logit Lens and entity-level probing, the findings were quite revealing. Superposition, the holy grail of continuous CoT reasoning, was evident only in models trained from scratch. The other two approaches fell short, with models either collapsing the superposition or bypassing it for shortcut solutions.

Unraveling the Mystery

So why does superposition falter in some models? The answer lies in the inherent biases of pre-trained models and their capacity. Models pre-trained on natural language tend to fixate on a single token in the final layers. Additionally, the capacity of a model significantly influences which solutions it prefers.

This understanding sheds light on a broader question: Are AI's reasoning capabilities as advanced as we think? If models default to shortcuts, the touted benefits of continuous CoT could be overstated for certain applications.

The Path Forward

For AI developers and researchers, this finding underscores the importance of training methodologies. Only through training models from scratch can the potential of superposition be fully realized. It's a clear call to action for those pushing the boundaries of AI reasoning.

The strategic bet is clearer than the street thinks. Continuous CoT reasoning holds promise, but without the right foundation, its benefits could remain theoretical. As AI continues its rapid evolution, the question is whether the industry will adapt its training approaches or continue to rely on less effective shortcuts.

The earnings call told a different story, but the real number to watch is how these models are being trained and the capabilities they truly harness.