arXiv:2605.06152v3 Announce Type: replace Abstract: Deep neural networks exhibit periodic loss spikes during unregularized long-term training, a phenomenon known as the "Slingshot Mechanism." Existing work usually attributes this to intrinsic optimization dynamics, but its triggering mechanism remains unclear. This paper proves that this phenomenon is a result of floating-point arithmetic precision limits. As training enters a high-confidence stage, the difference between the correct-class logit and the other logits may exceed the absorption-error threshold. Then during backpropagation, the gr
This research provides a new mechanistic explanation for 'Slingshot Mechanisms' in deep neural networks, linking it to the fundamental limitations of floating-point arithmetic rather than just optimization dynamics.
Understanding the root cause of these loss spikes is crucial for developing more stable and reliable AI models, especially as larger models increasingly push precision limits.
This shifts the understanding of a key AI training phenomenon from an optimization problem to a precision problem, influencing future hardware and software design for AI.
- · AI hardware manufacturers (precision optimization)
- · AI research scientists (model stability)
- · Cloud AI providers (predictable training)
- · Developers relying solely on current unoptimized training methods
AI model training stability could be improved by addressing floating-point precision issues.
New hardware designs or software frameworks might emerge that offer dynamic or higher precision to mitigate these issues.
This could contribute to the development of more robust general AI, as fundamental training instabilities are better understood and addressed.
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Read at arXiv cs.LG