Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling
Zeroth-Order optimization presents a promising memory-efficient paradigm for fine-tuning Large Language Models by relying solely on forward passes. However, its practical adoption is severely constrained by slow wall-clock convergence and high estimation variance. In this work, we dissect the runtime characteristics of ZO algorithms and identify a critical system bottleneck where the generation of perturbations and parameter updates accounts for over 40% of the training latency. We argue that the standard uniform exploration strategy is fundamentally flawed as it fails to account for the heterogeneous sensitivity of layers in deep networks, resulting in computationally wasteful blind sear...
| Authors | Fei Wang, Li Shen, Liang Ding et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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