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GradientStabilizer:Fix the Norm, Not the Gradient
arXiv – CS AI|Tianjin Huang, Zhangyang Wang, Haotian Hu, Zhenyu Zhang, Gaojie Jin, Xiang Li, Li Shen, Jiaxing Shang, Tianlong Chen, Ke Li, Lu Liu, Qingsong Wen, Shiwei Liu||2 views
🤖AI Summary
Researchers propose GradientStabilizer, a new technique to address training instability in deep learning by replacing gradient magnitude with statistically stabilized estimates while preserving direction. The method outperforms gradient clipping across multiple AI training scenarios including LLM pre-training, reinforcement learning, and computer vision tasks.
Key Takeaways
- →GradientStabilizer addresses gradient norm spikes that cause training instability in deep learning systems without requiring threshold tuning like gradient clipping.
- →The technique preserves gradient direction while replacing update magnitude with running statistical estimates, providing uniform bounds on spike steps.
- →Testing across LLM pre-training, quantization-aware training, ImageNet classification, and reinforcement learning shows consistent stability improvements.
- →The method widens stable learning-rate regions and reduces training divergence compared to clipping-based approaches.
- →GradientStabilizer reduces Adam optimizer's sensitivity to weight-decay strength, improving overall training robustness.
#deep-learning#gradient-optimization#training-stability#llm#machine-learning#optimizer#ai-research#neural-networks
Read Original →via arXiv – CS AI
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