HARP: Efficient Data Selection for Finetuning Large Language Models
Finetuning data selection requires balancing two competing goals: selecting examples that improve the downstream objective, and doing so without repeatedly finetuning models. Train-free selectors are scalable but rely on proxies such as embedding similarity or clustering, which may not match the target objective. Train-based selectors better reflect downstream utility through gradient signals, subset evaluation, or Shapley attribution, but require many costly train-evaluate iterations. We propose Hierarchical Active Region Pruning (HARP), an efficient train-based selector that preserves downstream alignment while reducing selection cost. HARP organizes the training pool into a node-leaf h...
| Authors | Ning Wang, Zhengxin Zhang, Maosen Tang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 5 June 2026 |
| Last updated at source | 13 October 2026 |
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