Exploring Latent Reasoning in Large Language Models
The traditional approach to training large language models (LLMs) relies on generating reasoning processes in natural language, known as chain-of-thought (CoT) reasoning. However, recent research suggests that constraining reasoning to language tokens may not always be optimal.
In “Training Large Language Models to Reason in a Continuous Latent Space” by Shibo Hao et al. (2024), the authors introduce a novel paradigm called Coconut (Chain of Continuous Thought). This approach enables LLMs to perform reasoning in an unrestricted latent space, rather than being confined to natural language. By utilizing the model’s last hidden state as a “continuous thought” and feeding it back as the subsequent input embedding, Coconut allows the model to explore multiple reasoning paths simultaneously. This breadth-first search capability enhances problem-solving efficiency and reduces premature commitments to single deterministic paths.
The authors argue that planning — a critical component of complex reasoning — benefits significantly from latent space representations over explicit natural language steps. Traditional token-based reasoning requires models to commit to specific thought sequences too early, limiting their ability to backtrack or reconsider alternative strategies. In contrast, latent space planning allows for non-linear, parallel exploration of different possibilities, leading to more flexible and adaptive decision-making. This approach makes it easier for models to adjust plans dynamically rather than being locked into a rigid sequence of steps. Additionally, latent representations can encode more abstract, high-dimensional information, helping models better generalize across diverse problem domains.
To me, this paper is interesting because it challenges the conventional reliance on language-based reasoning in AI models. By proposing a shift to latent space reasoning, the authors open new avenues for developing more flexible and efficient AI systems capable of tackling complex problems with greater agility. I am particularly interested in understanding what type of tasks are better tackled using this approach instead of the more traditional token-based reasoning (see last section of the paper).
What are your thoughts on the potential of latent space reasoning to redefine AI’s approach to complex problem-solving?
