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-level counterfactual utility prediction and introduce GRASP, an interaction-aware surrogate. Grounded in a theoretical smoothness lower bound, GRASP explicitly models subset interactions through a quadratic geometric penalty. To achieve pretraining-scale efficiency without relying on hidden oracle tuning, we couple low-dimensional feature sketches with a strictly finite lower-confidence bound ...
| Authors | Yue Min, Ruining Chen, Yujun Li |
|---|---|
| Primary category | cs.LG |
| Published | 5 June 2026 |
| Last updated at source | 8 October 2026 |
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