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 \textbf{UniGeM}, a framework that unifies mixing and selection by treating data curation as a \textit{manifold approximation} problem without training proxy models or relying on external reference datasets. UniGeM operates hierarchically: \textbf{Macro-Exploration} learns mixing weights with stability-based clustering; \textbf{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 \textbf{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.

翻译：大型语言模型（LLM）的扩展日益受到数据质量的限制。大多数方法将数据混合与样本选择分开处理，这可能破坏代码语料库中的结构。我们提出了**UniGeM**框架，该框架通过将数据策展视为一个**流形逼近**问题，统一了混合与选择过程，无需训练代理模型或依赖外部参考数据集。UniGeM分层操作：**宏观探索**通过基于稳定性的聚类学习混合权重；**微观挖掘**根据实例的几何分布筛选高质量样本，以确保逻辑一致性。通过在1000亿个词元上训练8B和16B的MoE模型进行验证，UniGeM相比随机基线实现了**2.0倍的数据效率**，并且在推理密集型评估和多语言泛化任务中，相比最先进方法进一步提升了整体性能。