Training Large Language Models to Reason in Continuous Latent Space

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LLMs have typically been restricted to reason in the "language space," where chain-of-thought (CoT) is used to solve complex reasoning problems. But a new paper argues that language space may not always be the best for reasoning. In this paper read, we cover an exciting new technique from a team at Meta called Chain of Continuous Thought—also known as "Coconut." In the paper, "Training Large Language Models to Reason in a Continuous Latent Space" explores the potential of allowing LLMs to reason in an unrestricted latent space instead of being constrained by natural language tokens.
Read a full breakdown of Coconut on our blog

Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

46 قسمت

#Science #Tech #Math #Business #Arize AI

Deep Papers

Training Large Language Models to Reason in Continuous Latent Space

Deep Papers

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published حدود یک سال پیش

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Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

46 قسمت

#Science #Tech #Math #Business #Arize AI

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Training Large Language Models to Reason in Continuous Latent Space

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Training Large Language Models to Reason in Continuous Latent Space