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

TL;DR

DART introduces a supervised framework that adaptively truncates reasoning in large language models based on problem difficulty, significantly improving efficiency while maintaining or enhancing accuracy across mathematical benchmarks.

Contribution

It presents a novel difficulty-adaptive reasoning truncation method that learns when to stop thinking, improving efficiency without sacrificing accuracy in LLM reasoning tasks.

Findings

Achieves 81.2% reasoning truncation on GSM8K dataset.

Provides 5.33× computational acceleration.

Maintains or improves reasoning accuracy.

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.