arXiv cs.CL
· Papers
Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Models
arXiv:2607.28707v1 Announce Type: new Abstract: Entropy-based pruning has been proposed as an effective method for compressing Chain-of-Thought (CoT) reasoning with negligible accuracy loss. We test the robustness of low- and high-entropy CoT step selection methods across various models and reasoning tasks, showing tha