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    <abstract>Chain-of-Thought (CoT) distillation transfers multi-step reasoning from large reasoning models to smaller students, but verbose teacher traces inflate both training and inference cost. Existing CoT compression methods fall into two families, selective pruning and generative rewriting, yet prior studies have left key factors entangled: granularity is confounded with importance criteria in pruning, restructuring level is rarely isolated in rewriting, and compression budgets are not systematically evaluated across domains or regimes. We recast CoT compression along three dimensions: importance criterion, restructuring level, and compression budget. Sweeping these across two model families, Math and General domains, and Long-/Short-CoT regimes, we find that (i) importance criterion utility is strictly governed by granularity: step-level criteria converge on a shared reasoning backbone, while token-level pruning requires symbol-aware signals to preserve the logical core; (ii) restructuring level inverts across domains: Math degrades monotonically with structural disruption, while aggressive rewriting acts as a denoiser on General tasks; (iii) training-time compression does not necessarily translate to inference-time savings: Long-CoT students retain verbose habits despite concise supervision, making the training ratio an optimistic lower bound on deployment cost. These findings yield condition-aware guidelines for matching compression to deployment context.</abstract>
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%0 Conference Proceedings
%T When Compression Helps and When It Hurts: Condition-Aware Analysis of Chain-of-Thought Distillation
%A Lyu, Siyang
%A Chen, Xinghao
%A Sun, Zhijing
%A Liu, Tong
%A Zhu, Dawei
%A Shen, Xiaoyu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lyu-etal-2026-compression
%X Chain-of-Thought (CoT) distillation transfers multi-step reasoning from large reasoning models to smaller students, but verbose teacher traces inflate both training and inference cost. Existing CoT compression methods fall into two families, selective pruning and generative rewriting, yet prior studies have left key factors entangled: granularity is confounded with importance criteria in pruning, restructuring level is rarely isolated in rewriting, and compression budgets are not systematically evaluated across domains or regimes. We recast CoT compression along three dimensions: importance criterion, restructuring level, and compression budget. Sweeping these across two model families, Math and General domains, and Long-/Short-CoT regimes, we find that (i) importance criterion utility is strictly governed by granularity: step-level criteria converge on a shared reasoning backbone, while token-level pruning requires symbol-aware signals to preserve the logical core; (ii) restructuring level inverts across domains: Math degrades monotonically with structural disruption, while aggressive rewriting acts as a denoiser on General tasks; (iii) training-time compression does not necessarily translate to inference-time savings: Long-CoT students retain verbose habits despite concise supervision, making the training ratio an optimistic lower bound on deployment cost. These findings yield condition-aware guidelines for matching compression to deployment context.
%U https://aclanthology.org/2026.emnlp-main.1543/
%P 35204-35231

Markdown (Informal)

[When Compression Helps and When It Hurts: Condition-Aware Analysis of Chain-of-Thought Distillation](https://aclanthology.org/2026.emnlp-main.1543/) (Lyu et al., EMNLP 2026)

• When Compression Helps and When It Hurts: Condition-Aware Analysis of Chain-of-Thought Distillation (Lyu et al., EMNLP 2026)

ACL

• Siyang Lyu, Xinghao Chen, Zhijing Sun, Tong Liu, Dawei Zhu, and Xiaoyu Shen. 2026. When Compression Helps and When It Hurts: Condition-Aware Analysis of Chain-of-Thought Distillation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 35204–35231, Budapest, Hungary. Association for Computational Linguistics.