Coconut turns LLM reasoning into continuous latent thoughts for better search
PMinervini · x · 2026-07-29
Continuous latent reasoning may outperform token-level CoT on search-heavy tasks
The post shares a reading path around latent reasoning and anchors it on the arXiv paper Training Large Language Models to Reason in a Continuous Latent Space.
Key points from the paper:
- It proposes Coconut (Chain of Continuous Thought), where the model uses its last hidden state as a continuous thought instead of decoding intermediate reasoning into words.
- That latent state is fed back as the next input embedding, letting the model reason in continuous space.
- The authors argue this can encode multiple possible next steps and behave more like breadth-first search than the single-path commitment of standard chain-of-thought.
- The paper reports better performance than CoT on logical reasoning tasks that require substantial planning/search, with a better trade-off between reasoning quality and efficiency.
The thread also highlights several adjacent papers on latent reasoning, including theory work on superposition, a formal comparison between CoT and latent thought, and a survey of the broader space.
Related event: Latent Reasoning Emerges as Alternative to Token-level CoT(2 posts)→
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