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This paper introduces GLOBE, a trajectory-aligned coreset selection framework that uses gradient trajectories across multiple checkpoints and multi-order matching with structured sparse optimization to select compact, representative training subsets, outperforming existing methods on six benchmarks.
Proposes SNAP-FM, a method that leverages sparse GPU nonlinear optimization to accelerate constraint projection in physics-constrained generative modeling, achieving faster inference while preserving exact physical constraint satisfaction.