arXiv:2506.04805v2 Announce Type: replace Abstract: Loss spikes commonly emerge during neural network training with the Adam optimizer across diverse architectures and scales, yet their underlying mechanism remains elusive. While previous explanations attribute these phenomena to sharper loss landscapes at lower loss, we show that landscape geometry alone is insufficient to explain the phenomenon. In this work, we pinpoint the root cause in the internal dynamics of Adam's second moment estimator. We identify a critical ``decoupling'' mechanism where the adaptive preconditioner $v_t$ fails to t
The increasing scale and complexity of neural networks highlight the limitations of current optimization methods, pushing researchers to uncover root causes of training instability.
Understanding and mitigating 'loss spikes' in Adam, a widely used optimizer, is crucial for developing more stable, efficient, and reliable large-scale AI models.
The identification of the second-moment estimator's 'decoupling' mechanism as the root cause provides a new theoretical foundation for improving adaptive optimization and neural network training.
- · AI researchers
- · Deep learning practitioners
- · Hardware manufacturers (indirectly through better utilization)
- · Developers relying on unoptimized Adam
- · Large model training projects prone to instability
New research will focus on redesigning Adam's second moment estimator to prevent decoupling.
Improved optimizers will lead to more stable and faster training of larger and more complex AI models.
These advancements could reduce the computational resources and time required for AI development, potentially accelerating AI progress.
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Read at arXiv cs.LG