用长推理链替代难题，仅1000样本就让模型性能大幅跃升。
Long Is More Important Than Difficult for Training Reasoning Models
- 用长推理路径替代高难度问题，提升模型训练效果。
- 推理数据长度每增长，模型性能呈对数线性提升，1000样本即达95.6%准确率。
- 开源长推理数据集与模型，适合高效训练推理能力的研究者。
高难度问题常被认为能有效提升推理模型性能，但此类问题稀缺，限制了训练数据规模。本文提出一种简单方法，解耦对问题难度的依赖。首先，实证发现推理长度比问题难度更能决定模型表现；其次，揭示推理长度的缩放规律：模型性能随推理数据长度增长呈对数线性提升；最后，提出一种生成任意长度推理数据的简单技术，验证其有效性。在Long1K数据集上微调Qwen2.5-32B-Instruct模型后，得到Long1K-32B模型，仅用1,000个样本即在MATH上达到95.6%准确率，在GPQA上达71.1%，优于DeepSeek-R1-Distill-Qwen-32B。模型、代码与数据集均开源。
原文摘要 · Abstract (English)
Difficult problems, which often result in long reasoning traces, are widely recognized as key factors for enhancing the performance of reasoning models. However, such high-challenge problems are scarce, limiting the size of available datasets. In this paper, we propose a simple method to decouple the reliance on problem difficulty. First, we empirically demonstrate that reasoning length, rather than problem difficulty, primarily influences the performance of trained models. Second, we identify a scaling law on reasoning length, showing that model performance increases in a log-linear fashion as the reasoning data length grows. Finally, we introduce a straightforward technique to generate reasoning data of arbitrary length, and show that synthesized data is effective for training reasoning models. After fine-tuning the Qwen2.5-32B-Instruct language model on our Long1K dataset, we present our model, Long1K-32B, which achieves remarkable performance with only 1,000 training samples, achieving 95.6\% accuracy on MATH, and 71.1\% on GPQA outperforming DeepSeek-R1-Distill-Qwen-32B. The model, code, and dataset are all open-sourced, available at https://huggingface.co/ZTss/LONG1.
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