Accelerating Zeroth-Order Spectral Optimization with Partial Orthogonalization from Power Iteration
Zeroth-order (ZO) optimization has become increasingly popular and important in fine-tuning large language models (LLMs), especially on edge devices due to its ability to adjust the model to local data without the need for memory-intensive back-propagation. Recent works try to reduce ZO variance through low-dimensional subspace search, but subspace restriction alone leaves key optimization geometry under-exploited, motivating additional acceleration. In this work, we focus on the hidden layer training problem in which spectral optimizers like Muon outperform AdamW due to its ability to exploit weak spectral directions by orthogonalization. However, we have discovered that unlike in the fi...
| Authors | Jiahe Chen, Ziye Ma |
|---|---|
| Primary category | cs.LG |
| Published | 9 May 2026 |
| Last updated at source | 15 May 2026 |
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