arXiv:2502.17055v4 Announce Type: replace Abstract: Training instability in modern deep learning systems is frequently triggered by rare but extreme gradient-norm spikes, which can induce oversized parameter updates, corrupt optimizer state, and lead to slow recovery or divergence. Widely used safeguards such as gradient clipping mitigate these failures but require threshold tuning and indiscriminately truncate large updates. We propose GradientStabilizer, a lightweight, drop-in gradient transform that preserves the instantaneous gradient direction while replacing the update magnitude with a s
The continuous drive to improve deep learning efficiency and stability, especially with larger models, makes solutions like GradientStabilizer highly relevant.
This development can significantly enhance the training stability and reliability of large AI models, reducing computational waste and improving model performance. Strategic readers should note the potential for more efficient AI development and deployment.
The method of handling gradient instability in deep learning could shift from clipping (threshold-dependent) to more adaptive, direction-preserving transforms like GradientStabilizer, leading to more robust training processes.
- · AI model developers
- · Cloud providers (reduced compute waste)
- · Deep learning researchers
- · AI-dependent industries
- · Inefficient gradient clipping methods
Increased stability and efficiency in training large deep learning models.
Faster iteration cycles for AI model development and potentially larger, more complex models becoming feasible.
Accelerated progress in AI capabilities across various applications due to more reliable training.
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Read at arXiv cs.LG