Supervised fine-tuning (SFT) on chain-of-thought data is an essential post-training step for reasoning language models. Standard machine learning intuition suggests that training with more unique training samples yields better generalization. Counterintuitively, we show that SFT benefits from repetition: under a fixed update budget, training for more epochs on smaller datasets outperforms single-epoch training on larger datasets. On AIME'24/25 and GPQA benchmarks, Olmo3-7B trained for 128 epochs on 400 samples outperforms the equivalent 1 epoch on 51200 samples by 12-26 percentage points, with no additional catastrophic forgetting. We find that training token accuracy reliably signals when repetition has saturated; improvements from additional epochs plateau at full memorization, a pattern consistent across all settings. These findings provide a practical approach for reasoning SFT, where scaling epochs with token accuracy as a stopping criterion can replace expensive undirected data scaling. We pose the repetition advantage, where full memorization coincides with improved generalization, as a new open problem for the community in understanding the training dynamics of large language models.

翻译：在链式思维数据上进行监督微调（SFT）是推理语言模型训练后不可或缺的关键步骤。标准机器学习直觉认为，使用更多独特训练样本进行训练能带来更好的泛化能力。然而反直觉的是，本研究表明SFT能从数据重复中获益：在固定更新预算下，对小规模数据集进行多轮次训练的效果优于对大规模数据集进行单轮次训练。在AIME'24/25和GPQA基准测试中，使用400个样本进行128轮训练的Olmo3-7B模型，其表现比使用51200个样本进行单轮训练的等效模型高出12-26个百分点，且未出现额外的灾难性遗忘现象。我们发现训练标记准确率能可靠指示重复训练的饱和点：当达到完全记忆时，增加训练轮次带来的改进会进入平台期，这一规律在所有实验设置中保持一致。这些发现为推理SFT提供了实用方法——通过扩展训练轮次并以标记准确率作为停止标准，可以替代昂贵的无定向数据扩展。我们提出"重复优势"这一新开放性问题：完全记忆与泛化能力提升同时发生的现象，需要学界共同探索以深入理解大语言模型的训练动态。