aiqbench analysis
This research addresses a critical bottleneck in LLM pretraining by introducing MSign, an optimizer that mitigates gradient explosions through stable rank restoration. Theoretically grounded and experimentally validated across models ranging from 5M to 3B parameters, the method achieves stability with minimal computational overhead under 7%. This work offers a practical solution for scaling deep networks without sacrificing training efficiency.
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 $\mu$P, 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%.
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 $\mu$P, 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%.
Machine Learning (cs.LG)
arXiv:2602.01734 [cs.LG]
(or arXiv:2602.01734v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.01734 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 218 words
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