Entropy-based CoT pruning offers no advantage over random pruning; low-entropy token retention is only briefly effective, per tests across models and tasks.
Read the original at arxiv.org→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...
Original headline: "Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Models"
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- Aug 3, 04:00 UTC arXiv cs.CL lead source Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Models