arXiv:2605.28517v1 Announce Type: new Abstract: Stochastic gradient descent with momentum (SGDM) is one of the most widely used optimization algorithms in machine learning. While optimization properties of SGDM have been extensively studied in the literature, it remains insufficiently understood whether and when SGDM can generalize well to unseen data. In particular, it has been conjectured that while momentum accelerates training, it may degrade generalization. In this paper, we close this gap by developing a comprehensive generalization analysis of SGDM through the lens of algorithmic stabil
The continuous evolution of AI demands deeper theoretical understanding of core algorithms like SGDM, especially as models scale and their reliability becomes paramount.
Improved theoretical understanding of widely used AI optimization algorithms informs better model design, leading to more robust and generalizable AI systems that are critical for advanced applications.
This research provides a theoretical foundation for understanding SGDM's generalization capabilities, potentially enabling more principled development of AI models rather than relying solely on empirical tuning.
- · AI researchers
- · Machine learning engineers
- · Deep learning framework developers
- · Developers relying on purely empirical AI optimization
More theoretically sound and dependable AI models can be developed for critical applications.
This foundational work could accelerate progress in AI safety and interpretability, as generalization can be better understood.
Improved algorithmic stability insight might contribute to more efficient use of compute resources in training large AI models, indirectly impacting the compute supply chain.
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Read at arXiv cs.LG