Ke Zhang

Diagnosing and Correcting Geometric Bias in Diversity-Driven Code Instruction Data Selection

Diversity-driven data selection offers a scalable way to reduce code instruction-tuning costs, yet it relies on a fragile assumption: geometric spread in representation space should correspond to training-useful semantic coverage. In code data selection, our diagnosis reveals a geometric bias behind this assumption: de...