DART: Difficulty-Adaptive Reasoning Truncation for Efficient Large Language Models

• URL: http://arxiv.org/abs/2511.01170v1
• Date: Mon, 03 Nov 2025 02:41:20 GMT
• Title: DART: Difficulty-Adaptive Reasoning Truncation for Efficient Large Language Models
• Authors: Ruofan Zhang, Bin Xia, Zhen Cheng, Cairen Jian, Minglun Yang, Ngai Wong, Yuan Cheng,
• Abstract summary: textbfDART adjusts thinking length according to problem difficulty.<n>textbfTruncation framework learns when to stop thinking''
• Score: 36.962276192354174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adaptive reasoning is essential for aligning the computational effort of large language models (LLMs) with the intrinsic difficulty of problems. Current chain-of-thought methods boost reasoning ability but indiscriminately generate long explanations, leading to evident inefficiency. However, existing reinforcement learning approaches to adaptive thinking remain unstable and heavily reward-dependent. Here we propose \textbf{DART}, a supervised \textbf{D}ifficulty-\textbf{A}daptive \textbf{R}easoning \textbf{T}runcation framework that adjusts thinking length according to problem difficulty. By distilling concise reasoning patterns from stronger models, interpolating them into a continuum of reasoning styles, and curating optimal training data that balances correctness and compactness, DART learns when to ``stop thinking''. Across multiple mathematical benchmarks, experimental results demonstrate its remarkable efficiency while preserving or improving accuracy, achieving a significant 81.2\% reasoning truncation (DeepSeek-R1-Distill-Qwen-7B on GSM8K dataset) with 5.33$\times$ computational acceleration. DART provides a stable and general paradigm for efficient reasoning, advancing the development of adaptive intelligence in LLMs.

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