arXiv:2605.02701v2 Announce Type: replace-cross Abstract: We propose a robust gradient estimator based on per-sample gradient clipping and analyze its properties both theoretically and empirically. We show that the resulting method, per-sample clipped SGD (PS-Clip-SGD), achieves optimal in-expectation convergence rates for non-convex optimization problems under heavy-tailed gradient noise. Moreover, we establish high-probability convergence guarantees that match the in-expectation rates up to polylogarithmic factors in the failure probability. We complement our theoretical results with multipl
The paper leverages recent advancements in robust optimization techniques and the increasing scale of AI models to address efficiency and stability during training.
Improved gradient estimators create more robust and faster training processes for AI models, especially in non-convex and noisy environments, which is crucial for foundational model development.
AI model training can become more stable and efficient, reducing computational costs and time for development, particularly for large-scale and complex models.
- · AI compute providers
- · Large language model developers
- · Deep learning researchers
- · AI-driven product companies
- · Inefficient AI training methods
- · High-compute-cost AI development
Faster and more reliable AI model development, leading to quicker iteration cycles.
Reduced barriers to entry for developing complex AI models due to lower training costs and improved stability.
Acceleration of AI research and deployment across various sectors, potentially democratizing access to powerful AI capabilities.
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Read at arXiv cs.LG