CoLT: Reasoning with Chain of Latent Tool Calls

Understand

Chain-of-Thought (CoT) is a critical technique in enhancing the reasoning ability of Large Language Models (LLMs), and latent reasoning methods have been proposed to accelerate the inefficient token-level reasoning chain.

• We notice that existing latent reasoning methods generally require model structure augmentation and exhaustive training, limiting their broader applicability.
• In this paper, we propose CoLT, a novel framework that implements latent reasoning as ``tool calls''.
• Instead of reasoning entirely in the latent space, CoLT generates seed tokens that contain information of a reasoning step.