@inproceedings{chen-etal-2025-revisiting,
    title = "Revisiting Scaling Laws for Language Models: The Role of Data Quality and Training Strategies",
    author = "Chen, Zhengyu  and
      Wang, Siqi  and
      Xiao, Teng  and
      Wang, Yudong  and
      Chen, Shiqi  and
      Cai, Xunliang  and
      He, Junxian  and
      Wang, Jingang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1163/",
    pages = "23881--23899",
    ISBN = "979-8-89176-251-0",
    abstract = "Traditional scaling laws in natural language processing suggest that increasing model size and training data enhances performance. However, recent studies reveal deviations, particularly in large language models, where performance improvements decelerate{---}a phenomenon known as sub-scaling. This paper revisits these scaling laws by examining the impact of data quality and training strategies on model performance. Through extensive empirical analysis of over 400 models, we identify high data density and non-optimal resource allocation as key factors contributing to sub-scaling. High data density leads to diminishing returns due to redundant information, while optimal resource allocation is crucial for sustained performance improvements. We propose a sub-optimal scaling law that better predicts performance in sub-scaling regimes, highlighting the importance of data quality and diversity."
}