Supervised fine-tuning (SFT) is a fundamental post-training strategy to align Large Language Models (LLMs) with human intent. However, traditional SFT often ignores the one-to-many nature of language by forcing alignment with a single reference answer, leading to the model overfitting to non-core expressions. Although our empirical analysis suggests that introducing multiple reference answers can mitigate this issue, the prohibitive data and computational costs necessitate a strategic shift: prioritizing the mitigation of single-reference overfitting over the costly pursuit of answer diversity. To achieve this, we reveal the intrinsic connection between token probability and semantic importance: high-probability tokens carry the core logical framework, while low-probability tokens are mostly replaceable expressions. Based on this insight, we propose ProFit, which selectively masks low-probability tokens to prevent surface-level overfitting. Extensive experiments confirm that ProFit consistently outperforms traditional SFT baselines on general reasoning and mathematical benchmarks.

翻译：监督微调（SFT）是一种使大型语言模型（LLM）与人类意图对齐的基础性后训练策略。然而，传统SFT通常忽略语言的一对多本质，强制模型与单一参考答案对齐，导致模型过度拟合非核心表达。尽管我们的实证分析表明引入多个参考答案可以缓解此问题，但高昂的数据和计算成本要求策略性转变：优先缓解单参考过拟合，而非追求代价高昂的答案多样性。为此，我们揭示了标记概率与语义重要性之间的内在联系：高概率标记承载核心逻辑框架，而低概率标记多为可替换表达。基于此发现，我们提出ProFit方法，通过选择性掩码低概率标记来防止表层过拟合。大量实验证实，ProFit在通用推理和数学基准测试中持续优于传统SFT基线方法。