PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning

• URL: http://arxiv.org/abs/2602.11639v1
• Date: Thu, 12 Feb 2026 06:43:08 GMT
• Title: PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning
• Authors: 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, Shuo Shang,
• Abstract summary: Language Reasoning Models (LRMs) achieve strong performance by scaling test-time computation but often suffer from overthinking''<n>We propose textbfmodel, a dual-level framework for prefix-protected and difficulty-aware compression under hierarchical supervision.
• Score: 37.125266434955584
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 \textbf{\model}, 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 \model achieves a substantial reduction in token usage (up to \textbf{55.7\%}) while simultaneously improving accuracy (up to \textbf{4.1\%}) on math benchmarks, with generalization ability to code, science, and general domains.

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