Let the Target Select for Itself: Data Selection via Target-Aligned Paths

Abstract:Targeted data selection seeks training examples from a candidate pool that improve downstream task performance. While trajectory-based selectors effectively guide this process, existing approaches typically construct reference states by warming up on the candidate pool itself, thereby inheriting pool-dependent distributional biases. To address this coupling, we introduce Target-Aligned Candidate Selection (TACS), which constructs a short, capacity-constrained reference path using a compact target-validation proxy. Candidates are ranked by their normalized loss reduction along this trajectory, requiring only forward passes and allowing the path to be reused across distinct pools for a fixed model-target pair. Empirically, TACS achieves competitive downstream performance across controlled logistic, vision, and NLP benchmarks, while slashing selection compute by up to 69% and candidate storage to less than 10 MB in large-scale instruction tuning.

Machine Learning (cs.LG); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2605.09404 [cs.LG]

(or arXiv:2605.09404v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.09404 Focus to learn more arXiv-issued DOI via DataCite

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