Zero-order fine-tuning stops sampling uniformly: query budget follows sample importance, up to 6x fewer queries to the same loss
Zero-order fine-tuning previously treated training examples alike; SAZO learns each example's importance online and allocates queries accordingly, improving average downstream performance across four model families and cutting the queries needed to reach the same training loss by up to 6x.