DRIFT: Data Selection for LLM Instruction Tuning via On-Policy Attribution.

arXiv:2606.18307v2 Announce Type: replace-cross Abstract: Data selection for instruction tuning is usually studied for efficiency, reaching strong performance with a fraction of the training data. We ask whether data selection can instead raise the performance