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

• URL: http://arxiv.org/abs/2511.00699v2
• Date: Tue, 04 Nov 2025 03:17:16 GMT
• Title: Inference-Time Chain-of-Thought Pruning with Latent Informativeness Signals
• Authors: Sophie Li, Nicholas Huang, Nayan Saxena, Nina Luo, Vincent Lin, Kevin Zhu, Sunishchal Dev,
• Abstract summary: Self-Truncation Best-of-N (ST-BoN) mitigates this by truncating unpromising paths early.<n>We present KL-Adjusted Pruned Path Algorithm (KAPPA), an inference-time method that combines Kullback-Leibler divergence, confidence, and entropy into a principled scoring function.
• Score: 6.5422130090856925
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) improve reasoning accuracy when generating multiple candidate solutions at test time, but standard methods like Best-of-N (BoN) incur high computational cost by fully generating all branches. Self-Truncation Best-of-N (ST-BoN) mitigates this by truncating unpromising paths early, but its reliance on consistency-based heuristics is a limitation as it does not directly evaluate branch quality. We present KL-Adjusted Pruned Path Algorithm (KAPPA), an inference-time method that combines Kullback-Leibler divergence, confidence, and entropy into a principled scoring function to guide progressive pruning. By promoting diversity during exploration and selectively eliminating low-scoring branches, KAPPA maintains accuracy while substantially reducing memory and token usage. Experiments on GSM8K and MATH500 with DeepSeek-R1-Distill-Qwen-1.5B and Qwen2.5-7B-Instruct demonstrate that KAPPA stabilizes performance in smaller models and achieves up to ~60% reduction in peak memory and ~90% reduction in total token generation relative to BoN, with minimal impact on accuracy.

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