Inference-Time Chain-of-Thought Pruning with Latent Informativeness Signals

arXiv — cs.LG•Wednesday, November 5, 2025 at 5:00:00 AM

A new approach named KL-Adjusted has been introduced to improve the efficiency of large language models during reasoning tasks. This method enhances the existing Self-Truncation Best-of-N technique, aiming to reduce computational costs while preserving high accuracy in generating candidate solutions. By leveraging latent informativeness signals, KL-Adjusted seeks to prune inference-time chains of thought more effectively. The primary goal of this approach is to balance computational efficiency with the quality of output, addressing challenges in large-scale language model reasoning. This development reflects ongoing efforts to optimize AI performance without compromising result reliability. The approach was detailed in a recent publication on arXiv under the cs.LG category.

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