Optimizing Deep Learning Models with SAM | Towards Data Science

Towards Data Science

by Anindya Dey

February 24, 2026

AI-Generated Deep Dive Summary

The article discusses the Sharpness-Aware-Minimization (SAM) optimizer, which enhances the generalizability of overparameterized deep learning models. Overparameterization allows models to memorize training data but often leads to poor performance on new data unless optimized correctly. SAM addresses this by improving how models generalize beyond just memorizing training sets. The piece explains that while larger models can theoretically generalize better past a certain threshold, they require careful optimization methods. Traditional optimizers might not suffice, and SAM stands out for its ability to optimize the loss function effectively, leading to improved test performance without increasing model size or complexity. The article also provides practical insights, including implementation details in PyTorch and considerations for models using BatchNorm layers. It highlights the importance of optimizing training algorithms to ensure models generalize well, which is crucial for real-world AI applications where data distribution shifts are common. Overall, SAM offers a valuable tool for researchers and practitioners aiming to build more robust and practical deep learning models. By focusing on optimization techniques like SAM, developers can enhance model reliability and effectiveness across various AI domains.

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Originally published on Towards Data Science on 2/24/2026