@inproceedings{wang-etal-2026-unigem,
    title = "{U}ni{G}e{M}: Unifying Data Selection and Mixing via Geometric Exploration and Mining",
    author = "Wang, Changhao  and
      Yunfeiyu  and
      Yao, Xinhao  and
      Yang, Jiaolong  and
      Yu, Lu  and
      Fang, Junpeng  and
      Li, Chaobo  and
      Cantoro, Riccardo  and
      Cui, Qing  and
      Zhou, Jun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1037/",
    pages = "20696--20714",
    ISBN = "979-8-89176-395-1",
    abstract = "The scaling of Large Language Models (LLMs) is increasingly limited by data quality. Most methods handle data mixing and sample selection separately, which can break the structure in code corpora. We introduce UniGeM, a framework that unifies mixing and selection by treating data curation as a manifold approximation problem without training proxy models or relying on external reference datasets. UniGeM operates hierarchically: Macro-Exploration learns mixing weights with stability-based clustering; Micro-Mining filters high-quality instances by their geometric distribution to ensure logical consistency. Validated by training 8B and 16B MoE models on 100B tokens, UniGeM achieves 2.0 $\times$ data efficiency over a random baseline and further improves overall performance compared to SOTA methods in reasoning-heavy evaluations and multilingual generalization."
}