arXiv:2606.19179v1 Announce Type: cross Abstract: Stochastic momentum methods such as heavy ball (HB), Nesterov momentum, and variants of Accelerated SGD (ASGD) [Kidambi et al., 2018] are widely used in modern training, but their stochastic benefits depend on two distinct quantities: serial runtime, the number of iterations needed to reach a target accuracy, and compute efficiency (CE), the inverse total gradient-query or FLOP cost. Larger batches reduce serial runtime without hurting CE only when the contraction gap grows linearly with batch size. We study stochastic HB and ASGD for consisten
The continuous growth in scale and complexity of AI models necessitates more efficient training algorithms to manage increasing computational demands.
Optimizing compute efficiency in stochastic momentum methods directly impacts the cost and speed of developing and deploying advanced AI, thereby influencing AI accessibility and innovation pace.
New algorithmic approaches could lead to more resource-efficient AI training, enabling smaller organizations or regions with less compute infrastructure to compete.
- · AI researchers and developers
- · Cloud computing providers (reduced operational costs)
- · Companies with limited compute budgets
- · Inefficient AI training methods
More efficient AI training algorithms become standard, accelerating model development cycles.
Reduced training costs translate to more experimentation and broader applications of sophisticated AI models.
Democratization of advanced AI development, potentially leading to a more diverse global AI ecosystem.
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