AIBullisharXiv – CS AI · 15h ago6/10
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GEM: Geometric Entropy Mixing for Optimal LLM Data Curation
Researchers introduce GEM (Geometric Entropy Mixing), a novel framework for optimizing LLM training data composition by treating curation as a variational problem on hyperspheres rather than relying on traditional Euclidean clustering. The method achieves up to 1.2% improvements in downstream accuracy on 1.1B-parameter models and provides a more interpretable approach to semantic data organization.