X-SAM: Boosting Sharpness-Aware Minimization with Dominant-Eigenvector Gradient Correction
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917205492367360 |
|---|---|
| author | Duan, Hongru Chen, Yongle Guan, Lei |
| author_facet | Duan, Hongru Chen, Yongle Guan, Lei |
| contents | Sharpness-Aware Minimization (SAM) aims to improve generalization by minimizing a worst-case perturbed loss over a small neighborhood of model parameters. However, during training, its optimization behavior does not always align with theoretical expectations, since both sharp and flat regions may yield a small perturbed loss. In such cases, the gradient may still point toward sharp regions, failing to achieve the intended effect of SAM. To address this issue, we investigate SAM from a spectral and geometric perspective: specifically, we utilize the angle between the gradient and the leading eigenvector of the Hessian as a measure of sharpness. Our analysis illustrates that when this angle is less than or equal to ninety degrees, the effect of SAM's sharpness regularization can be weakened. Furthermore, we propose an explicit eigenvector-aligned SAM (X-SAM), which corrects the gradient via orthogonal decomposition along the top eigenvector, enabling more direct and efficient regularization of the Hessian's maximum eigenvalue. We prove X-SAM's convergence and superior generalization, with extensive experimental evaluations confirming both theoretical and practical advantages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_10251 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | X-SAM: Boosting Sharpness-Aware Minimization with Dominant-Eigenvector Gradient Correction Duan, Hongru Chen, Yongle Guan, Lei Machine Learning Artificial Intelligence Sharpness-Aware Minimization (SAM) aims to improve generalization by minimizing a worst-case perturbed loss over a small neighborhood of model parameters. However, during training, its optimization behavior does not always align with theoretical expectations, since both sharp and flat regions may yield a small perturbed loss. In such cases, the gradient may still point toward sharp regions, failing to achieve the intended effect of SAM. To address this issue, we investigate SAM from a spectral and geometric perspective: specifically, we utilize the angle between the gradient and the leading eigenvector of the Hessian as a measure of sharpness. Our analysis illustrates that when this angle is less than or equal to ninety degrees, the effect of SAM's sharpness regularization can be weakened. Furthermore, we propose an explicit eigenvector-aligned SAM (X-SAM), which corrects the gradient via orthogonal decomposition along the top eigenvector, enabling more direct and efficient regularization of the Hessian's maximum eigenvalue. We prove X-SAM's convergence and superior generalization, with extensive experimental evaluations confirming both theoretical and practical advantages. |
| title | X-SAM: Boosting Sharpness-Aware Minimization with Dominant-Eigenvector Gradient Correction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.10251 |