@inproceedings{feng-etal-2026-pace,
title = "{PACE}: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning",
author = "Feng, Ruixiang and
Wen, Yuntao and
Zhou, Silin and
Shi, Ke and
Wang, Yifan and
Le, Ran and
An, Zhenwei and
Chen, Zongchao and
Yang, Chen and
Peng, Guangyue and
Jia, Yiming and
Wang, Dongsheng and
Zhang, Tao and
Chen, Lisi and
Song, Yang and
Gao, Shen and
Shang, Shuo",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1545/",
pages = "30884--30903",
ISBN = "979-8-89176-395-1",
abstract = "Language Reasoning Models (LRMs) achieve strong performance by scaling test-time computation but often suffer from ``overthinking'', producing excessively long reasoning traces that increase latency and memory usage. Existing LRMs typically enforce conciseness with uniform length penalties, which over-compress crucial early deduction steps at the sequence level and indiscriminately penalize all queries at the group level. To solve these limitations, we propose PACE, a dual-level framework for prefix-protected and difficulty-aware compression under hierarchical supervision. At the sequence level, prefix-protected optimization employs decaying mixed rollouts to maintain valid reasoning paths while promoting conciseness. At the group level, difficulty-aware penalty dynamically scales length constraints based on query complexity, maintaining exploration for harder questions while curbing redundancy on easier ones. Extensive experiments on DeepSeek-R1-Distill-Qwen (1.5B/7B) demonstrate that PACE achieves a substantial reduction in token usage (up to 55.7{\%}) while simultaneously improving accuracy (up to 4.1{\%}) on math benchmarks, with generalization ability to code, science, and general domains."
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<abstract>Language Reasoning Models (LRMs) achieve strong performance by scaling test-time computation but often suffer from “overthinking”, producing excessively long reasoning traces that increase latency and memory usage. Existing LRMs typically enforce conciseness with uniform length penalties, which over-compress crucial early deduction steps at the sequence level and indiscriminately penalize all queries at the group level. To solve these limitations, we propose PACE, a dual-level framework for prefix-protected and difficulty-aware compression under hierarchical supervision. At the sequence level, prefix-protected optimization employs decaying mixed rollouts to maintain valid reasoning paths while promoting conciseness. At the group level, difficulty-aware penalty dynamically scales length constraints based on query complexity, maintaining exploration for harder questions while curbing redundancy on easier ones. Extensive experiments on DeepSeek-R1-Distill-Qwen (1.5B/7B) demonstrate that PACE achieves a substantial reduction in token usage (up to 55.7%) while simultaneously improving accuracy (up to 4.1%) on math benchmarks, with generalization ability to code, science, and general domains.</abstract>
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%0 Conference Proceedings
%T PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning
%A Feng, Ruixiang
%A Wen, Yuntao
%A Zhou, Silin
%A Shi, Ke
%A Wang, Yifan
%A Le, Ran
%A An, Zhenwei
%A Chen, Zongchao
%A Yang, Chen
%A Peng, Guangyue
%A Jia, Yiming
%A Wang, Dongsheng
%A Zhang, Tao
%A Chen, Lisi
%A Song, Yang
%A Gao, Shen
%A Shang, Shuo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F feng-etal-2026-pace
%X Language Reasoning Models (LRMs) achieve strong performance by scaling test-time computation but often suffer from “overthinking”, producing excessively long reasoning traces that increase latency and memory usage. Existing LRMs typically enforce conciseness with uniform length penalties, which over-compress crucial early deduction steps at the sequence level and indiscriminately penalize all queries at the group level. To solve these limitations, we propose PACE, a dual-level framework for prefix-protected and difficulty-aware compression under hierarchical supervision. At the sequence level, prefix-protected optimization employs decaying mixed rollouts to maintain valid reasoning paths while promoting conciseness. At the group level, difficulty-aware penalty dynamically scales length constraints based on query complexity, maintaining exploration for harder questions while curbing redundancy on easier ones. Extensive experiments on DeepSeek-R1-Distill-Qwen (1.5B/7B) demonstrate that PACE achieves a substantial reduction in token usage (up to 55.7%) while simultaneously improving accuracy (up to 4.1%) on math benchmarks, with generalization ability to code, science, and general domains.
%U https://aclanthology.org/2026.findings-acl.1545/
%P 30884-30903
Markdown (Informal)
[PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning](https://aclanthology.org/2026.findings-acl.1545/) (Feng et al., Findings 2026)
ACL
- Ruixiang Feng, Yuntao Wen, Silin Zhou, Ke Shi, Yifan Wang, Ran Le, Zhenwei An, Zongchao Chen, Chen Yang, Guangyue Peng, Yiming Jia, Dongsheng Wang, Tao Zhang, Lisi Chen, Yang Song, Shen Gao, and Shuo Shang. 2026. PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 30884–30903, San Diego, California, United States. Association for Computational Linguistics.