Data repetition outperforms data scaling for fine-tuning language models on chain-of-thought reasoning.
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.
Training language models on repeated data beats training once on larger datasets for chain-of-thought reasoning tasks.
Olmo3-7B trained 128 epochs on 400 samples outperformed single-epoch training on 51,200 samples by 12-26 percentage points.
High-quality reasoning data is scarce and expensive, making data repetition more practical than scaling for supervised fine-tuning.
Study focused only on three models and three benchmarks, limiting generalizability across different architectures and evaluation metrics.
Reasoning data from Dolci SFT dataset may not represent all types of training data or real-world applications.
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