GradientStabilizer:Fix the Norm, Not the Gradient 事件

GradientStabilizer:Fix the Norm, Not the Gradient arXiv:2502.17055v4 Announce Type: replace-cross 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 Gr