GIST: Targeted Data Selection for Instruction Tuning via Coupled Optimization Geometry

In three linesGIST introduces targeted data selection for instruction tuning by replacing axis-aligned scaling with robust subspace alignment via SVD. It recovers task-specific subspaces from validation gradients and scores examples by alignment with target directions. GIST matches or outperforms state-of-the-art baselines using only 0.29% storage and 25% computational time.

Summary generated by Claude — human-verified