Sample-Adaptive Zeroth-Order Post-Training
Sample-Adaptive Zeroth-Order Optimization (SAZO) improves memory-efficient fine-tuning of large language models by learning sample importance online. It estimates how important each training example is using existing directional differences, rather than needing true gradients. This adaptive sampling strategy accelerates convergence, achieving up to 6x faster training with the same query count and better downstream performance.