arXiv:2506.12444v2 Announce Type: replace-cross Abstract: In this paper, we propose Adjusted Shuffling SARAH, a novel algorithm that integrates shuffling strategies into the recursive SARAH framework using a dynamic weighting mechanism to enhance exploration. We analyze the algorithm under two operating modes. First, we show that the Exact Mode matches the best-known theoretical guarantees for shuffling variance-reduced methods in both strongly convex and non-convex settings. Second, to address large-scale regimes, we introduce an Inexact Mode that utilizes mini-batch estimators. A key contrib
The continuous evolution of AI algorithms necessitates constant improvement in optimization techniques to handle increasingly complex models and larger datasets effectively.
Improved optimization algorithms like Adjusted Shuffling SARAH can significantly accelerate AI training, making AI development more efficient and accessible, especially for large-scale applications.
This research provides a more efficient variance-reduced method for optimizing AI models, potentially leading to faster convergence and better performance in both convex and non-convex settings.
- · AI researchers
- · Large language model developers
- · Machine learning startups
- · Cloud computing providers
- · Organizations relying on less optimized algorithms
- · AI development with limited computational resources
Faster and more robust training of machine learning models will become possible.
This could lead to quicker iteration cycles for new AI capabilities and applications.
Advances in foundational AI optimization might indirectly accelerate the development of more complex AI systems like advanced AI agents.
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Read at arXiv cs.LG