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arXiv · 2510.01578

Gradient Shaping Beyond Clipping: A Functional Perspective on Update Magnitude Control

Haochen You·Baojing Liu
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Abstract

Gradient clipping is widely used to stabilize deep network training, but its formulation as a hard, fixed threshold limits flexibility and ignores gradient distribution dynamics. We propose SPAMP (Statistical Per-layer Adaptive Modulation and Projection), a unified framework that generalizes clipping into smooth, per-layer gradient shaping. SPAMP tracks local gradient statistics, dynamically estimates thresholds, and applies power-based transformations to modulate update magnitudes in a differentiable manner. This perspective recasts clipping and warmup as dual mechanisms for controlling the effective update scale $\eta_t \|g_t\|$, offering a principled alternative to rigid heuristics. Extensive experiments across image and language tasks demonstrate that SPAMP improves stability, convergence, and robustness over existing methods.

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Haochen You, Baojing Liu. 2025-10-02. Gradient Shaping Beyond Clipping: A Functional Perspective on Update Magnitude Control. https://arxiv.org/abs/2510.01578

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