An Empirical Study on Noisy Data and LLM Pretraining Loss Divergence

Large-scale pretraining datasets drive the success of large language models (LLMs). However, these web-scale corpora inevitably contain large amounts of noisy data due to unregulated web content or randomness inherent in data. Although LLM pretrainers often speculate that such noise contributes to instabilities in large-scale pretraining and, in the worst cases, loss divergence, this phenomenon remains poorly understood. In this work, we present a systematic empirical study of whether noisy data causes LLM pretraining divergences, costing more than 100,000 GPU hours in total. By injecting controlled, synthetic, uniform random noise into otherwise clean datasets, we analyze training dynamics across model sizes ranging from 480M to 5.2B parameters. We show that noisy data indeed induce training loss divergence, and that the probability of divergence depends strongly on the noise type, the amount of noise, and the model scale. We further find that noise-induced divergences exhibit activation patterns distinct from those caused by high learning rates, and we provide diagnostics that differentiate these two failure modes. Together, these results provide a large-scale, controlled characterization of how noisy data affects loss divergence in LLM pretraining.