Training Large Language Models to Reason in a Continuous Latent Space

Understand

Large language models (LLMs) are typically constrained to reason in the language space, where they express the reasoning process through a chain-of-thought (CoT) to solve complex problems.

• However, the language space may not always be optimal for reasoning.
• Most word tokens primarily ensure textual coherence and are not essential for reasoning, while some critical tokens require complex planning and pose challenges to LLMs.
• To explore the potential of reasoning beyond language, we introduce a new paradigm called Coconut (Chain of Continuous Thought).