arXiv:2605.26121v1 Announce Type: new Abstract: LLM pre-training efficacy increasingly depends on data composition rather than sheer volume. Yet, optimal mixing is hindered by categorization flaws: human taxonomies suffer from ontological misalignment, and Euclidean clustering fails to address embedding anisotropy. We introduce GEM (Geometric Entropy Mixing), a framework reformulating data curation as a variational problem on the hypersphere augmented with a mixing-balance regularizer. By decoupling the generative prior and optimizing the objective via a provable MM (Minorize-Maximize) algorit
The increasing scale of LLM pre-training highlights data quality and composition as critical bottlenecks, making optimal data curation a pressing research area.
This research introduces a novel, mathematically rigorous approach to LLM data curation, potentially leading to more efficient and powerful models with less raw data.
The paradigm for LLM data curation shifts from brute-force volume to sophisticated, geometrically-informed mixing, enabling better model performance and resource optimization.
- · AI model developers
- · Cloud computing providers
- · Data scientists
- · Organizations training custom LLMs
- · Companies reliant on sheer data volume
- · Traditional data labeling services
- · Inefficient LLM development pipelines
Improved efficiency and performance of large language models due to optimized training data.
Reduced computational costs and environmental footprint for training future generations of AI models.
Democratization of advanced AI capabilities by making high-performing models more accessible to those with limited data or compute resources.
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