MSign: An Optimizer Preventing Training Instability in Large Language Models via Stable Rank Restoration

arXiv:2602.01734v2 Announce Type: replace Abstract: Training instability remains a critical challenge in large language model (LLM) pretraining, often manifesting as sudden gradient explosions that waste significant computational resources. We study training failures in a 5M-parameter NanoGPT model scaled via muP, identifying two key phenomena preceding collapse: (1) rapid decline in weight matrix stable rank (ratio of squared Frobenius norm to squared spectral norm), and (2) increasing alignment between adjacent layer Jacobians. We prove theoretically that these two conditions jointly cause exponential gradient norm growth with network depth. To break this instability mechanism, we propose MSign, a new optimizer that periodically applies matrix sign operations to restore stable rank. Experiments on models from 5M to 3B parameters demonstrate that MSign effectively prevents training failures with a computational overhead of less than 7.0%.

Related

• AdaGC: Enhancing LLM Pretraining Stability via Adaptive Gradient Clipping
• SAGE: Sign-Adaptive Gradient for Memory-Efficient LLM Optimization
• Geometrically Principled Randomized Optimization for Efficient LLM Training

Source: arXiv cs.LG | 2026-09-04