@inproceedings{candussio-etal-2026-demystifying,
title = "Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Models",
author = "Candussio, Sara and
Scalena, Daniel and
Fersini, Elisabetta and
Bortolussi, Luca and
Nissim, Malvina and
Sarti, Gabriele",
editor = "Arad, Dana and
Belinkov, Yonatan and
Chen, Hanjie and
Feldhus, Nils and
Mueller, Aaron and
Mohebbi, Hosein and
Sarti, Gabriele and
Tutek, Martin",
booktitle = "Proceedings of the 9th {B}lackbox{NLP} Workshop: Analyzing and Interpreting Neural Networks for {NLP}",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.blackboxnlp-1.59/",
pages = "1047--1066",
ISBN = "979-8-89176-476-7",
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 that entropy offers no advantage over random pruning in any evaluated setting. Moving from steps to tokens, we then show that retaining low-entropy tokens seems effective only on mathematical benchmarks. We find this is due to the inherently low-entropy nature of numeric tokens, which also convey semantic content in such problems. Finally, we demonstrate that patching a subset of a few CoT tokens with their original activations recovers near-perfect full-trace performance, providing causal evidence that task information is not concentrated in a small set of CoT tokens identifiable by heuristics, but rather distributed across the full reasoning chain."
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%0 Conference Proceedings
%T Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Models
%A Candussio, Sara
%A Scalena, Daniel
%A Fersini, Elisabetta
%A Bortolussi, Luca
%A Nissim, Malvina
%A Sarti, Gabriele
%Y Arad, Dana
%Y Belinkov, Yonatan
%Y Chen, Hanjie
%Y Feldhus, Nils
%Y Mueller, Aaron
%Y Mohebbi, Hosein
%Y Sarti, Gabriele
%Y Tutek, Martin
%S Proceedings of the 9th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-476-7
%F candussio-etal-2026-demystifying
%X 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 that entropy offers no advantage over random pruning in any evaluated setting. Moving from steps to tokens, we then show that retaining low-entropy tokens seems effective only on mathematical benchmarks. We find this is due to the inherently low-entropy nature of numeric tokens, which also convey semantic content in such problems. Finally, we demonstrate that patching a subset of a few CoT tokens with their original activations recovers near-perfect full-trace performance, providing causal evidence that task information is not concentrated in a small set of CoT tokens identifiable by heuristics, but rather distributed across the full reasoning chain.
%U https://aclanthology.org/2026.blackboxnlp-1.59/
%P 1047-1066
Markdown (Informal)
[Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Models](https://aclanthology.org/2026.blackboxnlp-1.59/) (Candussio et al., BlackboxNLP 2026)
ACL