arXiv:2605.17659v2 Announce Type: replace Abstract: The design of modern neural architectures has converged through incremental empirical choices, yet the mechanisms governing their training dynamics remain only partially understood. We identify and analyze a negative weight drift induced by the interaction between standard losses and positively biased activation functions. We prove that under MSE or cross-entropy loss, the gradient with respect to positive pre-activations is non-negative in expectation at initialization, driving downstream weights toward negative values during early training.
The continuous evolution of neural network architectures necessitates deeper theoretical understanding to optimize performance and efficiency.
Understanding fundamental training dynamics like weight drift and activation sparsity can lead to more robust, efficient, and explainable AI models, impacting the entire AI development ecosystem.
This research provides a theoretical understanding of specific training dynamics, which could inform the design of future neural networks and lead to more predictable model behavior.
- · AI researchers
- · ML framework developers
- · Hardware manufacturers (indirectly through more efficient models)
- · Developers relying solely on empirical tuning without theoretical grounding
Improved understanding of neural network training stability and efficiency.
Development of new initialization schemes or regularization techniques that counteract negative weight drift.
More resource-efficient AI models, potentially reducing the energy and computational demands of large-scale AI research and deployment.
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Read at arXiv cs.LG