According to God of Prompt (@godofprompt), during training plateaus in AI models, the system is not simply stuck but actively reorganizing its internal representations. Over thousands of epochs, neural circuits form and dissolve, weight patterns stabilize, irrelevant correlations are eliminated, and meaningful structures gradually emerge. This process, akin to human insight, highlights how deep learning models refine their understanding before achieving sudden performance gains. These insights into model learning dynamics reveal practical opportunities for optimizing training strategies, enhancing model interpretability, and improving performance in real-world AI applications (Source: @godofprompt, Jan 6, 2026).