Sharpness-Aware Minimization (SAM)
Definition
SAM trains models on solutions that maintain low error even with small parameter changes.
An optimization method that searches for parameters whose loss remains low in a small neighborhood.
Simply explained
A setting is sought that works well not only at a single point but also in its vicinity.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
At each step, a local loss-increasing perturbation is first determined and then optimized against.
Limits and pitfalls
- Treating flatness as a coordinate-independent guarantee for generalization or ignoring computational overhead.
Origin & history
Foret and co-authors published SAM in 2020.
Use in mid-sized companies
- Possible example: At each step, a local loss-increasing perturbation is first determined and then optimized against.
Frequently asked questions
What does Sharpness-Aware Minimization (SAM) mean?
An optimization method that searches for parameters whose loss remains low in a small neighborhood. What mistakes should be avoided? Treating flatness as a coordinate-independent guarantee for generalization or ignoring computational overhead.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: