Researchers have proposed a solution to the problem of language model pretraining when there is limited data available. They suggest using training-time data augmentation as a regularizer to prevent overfitting. This involves adding noise or modifying the input in various ways during training, such as masking words or predicting sequences in reverse order. The authors tested three types of augmentation and found that combining them can significantly improve the performance of language models. --- Why it matters: This matters because it could help researchers train more effective language models with limited data, which is a common challenge in AI development. Source: https://arxiv.org/abs/2606.16246