The paper "Training Large Language Models to Reason in Continuous Latent Space" explores a new technique called Chain of Continuous Thought, also known as Coconut, which allows large language models to reason in an unrestricted latent space instead of being constrained by natural language tokens. This approach draws inspiration from human brain activity and enables the model to bypass its language centers, suggesting a more efficient way to process thoughts. The model operates in two modes: latent mode, where it represents thoughts using internal states, and language mode, where it provides a human-readable response. Coconut outperforms traditional chain of thought methods and other approaches in some cases, but is comparable to integrated chain of thought methods. It also uses fewer tokens than some approaches, making it a more efficient method, especially for complex problems. The model benefits from using at least three "thoughts" in its latent space, and future work is needed to further refine and scale latent reasoning methods.