@inproceedings{zhao-etal-2025-data,
    title = "More Data or Better Data? A Critical Analysis of Data Selection and Synthesis for Mathematical Reasoning",
    author = "Zhao, Yike  and
      Guo, Simin  and
      Yang, Ziqing  and
      Han, Shifan  and
      Lin, Dahua  and
      Tan, Fei",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.43/",
    pages = "618--629",
    ISBN = "979-8-89176-333-3",
    abstract = "The reasoning capabilities of Large Language Models (LLMs) play a critical role in many downstream tasks, yet depend strongly on the quality of training data. Despite various proposed data construction methods, their practical utility in real-world pipelines remains underexplored. In this work, we conduct a comprehensive analysis of open-source datasets and data synthesis techniques for mathematical reasoning, evaluating them under a unified pipeline designed to mirror training and deployment scenarios. We further distill effective data selection strategies and identify practical methods suitable for industrial applications. Our findings highlight that structuring data in more interpretable formats, or distilling from stronger models often outweighs simply scaling up data volume. This study provides actionable guidance for integrating training data to enhance LLM capabilities, supporting both cost-effective data curation and scalable model enhancement. We hope this work will inspire further research on how to balance ``more data'' versus ``better data'' for real-world reasoning tasks."
}