@inproceedings{liang-etal-2026-rsda,
    title = "{RSDA}: Restoring Stale Data Affinity via Dynamic Renovation Strategy for Mitigating Data Scarcity",
    author = "Liang, Yidan  and
      Zhu, Jia  and
      Shi, Weijie  and
      Guo, Hanghui  and
      Cui, Yue  and
      Shen, Jiawei  and
      Ma, Guoqing  and
      Liu, Jingjiang  and
      Niu, Qingyu  and
      Wang, Yilin  and
      Di, Shimin  and
      Xu, Jiajie",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.375/",
    pages = "8280--8309",
    ISBN = "979-8-89176-390-6",
    abstract = "High-quality data is the cornerstone of advancing large language models. However, the field currently faces a critical dilemma: the supply of premium data is nearing depletion, while vast stale corpora remain underutilized. Our empirical analysis reveals that training models on such data directly often leads to performance degradation. We attribute this phenomenon to the data affinity gap, a misalignment stemming from the model{'}s inability to effectively comprehend the data or inherent quality defects. To bridge this gap, we propose Restoring Stale Data Affinity (RSDA) framework. First, utilizing our proposed potential entropy metric, RSDA quantifies the latent value of samples to effectively identify stale data with higher renovation potential. Subsequently, the framework employs a dynamic renovation strategy selection mechanism to determine the optimal component-level strategy for each instance, transforming low-affinity stale samples into high-quality training data. Comprehensive experimental results demonstrate that RSDA effectively enhances data affinity, achieving performance improvements using less than 10{\%} of the data volume, thereby underscoring that the latent potential of stale corpora remains largely untapped. The code is available at https://github.com/wenfiii/RSDA."
}