The Limits of Chain of Thought in LLMs
Latent reasoning is ushering in a new era in AI thinking processes.
1The Limits of Chain of Thought
The Chain of Thought (CoT) method used in AI models produces readable traces but does not reflect actual computation. This shows that generating text is not the same as thinking. CoT traces can decouple from what the model actually did, leading to incorrect results.
2Latent Reasoning Method
Recent studies are shifting AI models' thinking processes into latent space and performing language analysis only at the final step. Methods like Coconut, HRM, and RecursiveMAS are examples of this new approach. These methods make thinking processes more efficient.
3The Relationship Between Language and Computation
While language is a fundamental tool for communication and abstraction, converting search and constraint-solving processes into text is challenging and costly. Latent reasoning makes these processes more fluid and economical, enhancing AI model performance.
4The Black Box Problem
Thinking processes occurring in latent loops eliminate the limited visibility provided by CoT. This poses a barrier, especially for use in high-stakes production environments. Outer loop governance and auditable plans are among the proposed solutions to this problem.
5The Role of BDH
BDH (Dragon Hatchling) takes a significant step in this field by retaining language modeling capability while adding latent computation. BDH achieves high accuracy rates in constraint-solving problems like Sudoku and aims to enhance general reasoning abilities.
6Future Directions
With the rise of latent reasoning, how we audit AI models' thinking processes becomes a crucial question. DAGs, unit tests, and formal specifications are among the methods proposed to manage these processes.
Original article: r/MachineLearning — LLM'lerde Zincirleme Düşünce Yönteminin Sınırları