ICLR Paper: Physics Theorems Reveal How Gradient Noise Shapes AI Representations
burny_tech · x · 2026-08-08
Researcher Liu Ziyin shared their ICLR 2024 work exploring the training dynamics of AI models from a theoretical physics perspective.
The study shows that gradient noise is a key determinant in how models learn representations. Drawing on fluctuation-dissipation theorems from physics, the research reveals that gradient noise, representations, and weights become mutually aligned during training. This serves as a compelling example of how physics can offer surprising predictions for AI model phenomenology.
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