Let the Target Select for Itself: Data Selection via Target-Aligned Paths
For practitioners needing efficient data selection for specific tasks, this method offers a simpler, cheaper alternative to existing dynamic attribution approaches.
The paper proposes a data selection method that uses a validation-induced flow from a short warmup on target data to score candidates, reducing reference path bias. It achieves competitive performance with dynamic attribution baselines while lowering warmup and storage costs.
Targeted data selection aims to identify training samples from a large candidate pool that improve performance on a specific downstream task. Many recent methods estimate candidate utility by aggregating local attribution scores along a trajectory induced by the candidate pool. When the pool is heterogeneous, however, this reference trajectory may be misaligned with the dynamics of a target-aligned selected subset, creating what we call reference path bias. We propose an alternative reference path: a validation-induced flow obtained from a short, capacity-limited warmup on the available target validation proxy. Along this path, candidates are scored by a normalized endpoint loss drop, yielding a simple zero-order selection rule that requires no candidate gradients or Hessian approximations. Across controlled logistic, vision, and instruction-tuning experiments, this score is competitive with strong dynamic attribution baselines while substantially reducing warmup and storage cost. Moreover, since the reference trajectory is decoupled from any specific candidate pool, the same compact warmup can be reused across additional pools without recomputing the trajectory.