Research Problem: The paper addresses the limitations of large language models (LLMs) reasoning purely in the "language space" and explores the potential of reasoning in an unrestricted latent space.
Key Contributions: Introduction of COCONUT (Chain of Continuous Thought), a new paradigm where LLMs reason using continuous hidden states instead of discrete word tokens. This allows for encoding multiple potential reasoning steps and performing a breadth-first search (BFS)-like exploration.
Methodology/Approach: COCONUT utilizes the last hidden state of the LLM as a "continuous thought" and feeds it back as the next input embedding. A multi-stage training strategy is used to guide the learning process, inspired by iCoT.
Results: COCONUT outperforms CoT in certain logical reasoning tasks, especially those requiring substantial backtracking, while generating fewer tokens during inference.
Discussion Points
Strengths:
Novel idea of reasoning in a continuous latent space.
Potential for more efficient reasoning by encoding multiple possibilities.
BFS-like exploration pattern is interesting.
The concept of preserving the probability distribution is valuable.
Weaknesses:
The comparison of token counts between COCONUT and CoT might not be entirely fair due to differences in how they operate.
The claim that the probability distribution represents an implicit value function is a bit of an overstatement.
The paper lacks rigorous experimental support for some claims.
The need for multi-stage training (guidance) raises questions about whether the model is truly learning to reason in the latent space or just following the training data.
The difference in meaning between the initial embedding space and the latent space after the final layer makes it difficult for the model to directly understand the latent space without fine-tuning.
The model is weight-dependent, meaning the latent space representation changes if the weights are updated.
Key Questions:
Is the comparison of token counts a fair measure of efficiency?
How does the model determine when to switch between latent and language modes during inference?
Is the model truly learning to reason in the latent space, or is it just mimicking the training data?
Can the difference in meaning between the initial embedding space and the latent space be addressed?
How does the weight-sharing between the top and bottom layers in GPT affect the compatibility of the embedding spaces?
Applications:
Potential applications in tasks requiring complex reasoning and planning.
Could be used to develop more dynamic and flexible reasoning architectures.
Connections:
Relates to other work on chain-of-thought reasoning, knowledge distillation, and multi-token prediction.
Connects to our interest in exploring alternative reasoning mechanisms for LLMs.
Notes and Reflections
Interesting Insights:
The emergence of a BFS-like reasoning pattern in the latent space.
The idea of using continuous thoughts to represent multiple potential reasoning paths.
The potential to make reasoning more dynamic by allowing the model to decide when to switch between modes.
Lessons Learned:
The importance of carefully considering the experimental design and the fairness of comparisons.
The need for more rigorous evidence to support claims about model behavior.
The challenges of training models to reason in a latent space.
Future Directions:
Further investigation into the properties of the latent space and how it encodes reasoning.
Exploring alternative training strategies that do not rely on language supervision.
Combining COCONUT with other techniques like residual networks to enhance context preservation.
Investigating the use of the probability distribution of the latent space to guide the reasoning process.
Applying COCONUT to a wider range of reasoning tasks.