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GRASP: Geometry-aware Residual Alignment for Scalable Pretraining Data Attribution
Scalable data attribution methods typically assign isolated utility scores to individual training examples. This prevalent additive assumption fundamentally fails to capture critical subset dynamics, including data redundancy and complementary coverage. In this work, we reframe attribution as subset
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