According to God of Prompt, the grokking phenomenon explains why certain AI fine-tuning runs, after a period of stagnation, suddenly achieve significant improvements. This effect is being observed in reinforcement learning from human feedback (RLHF) where models internalize human preferences, in domain adaptation for specialized large language models (such as those for medical and legal industries), and in advanced reasoning models like o1 and DeepSeek-R1. Prolonged training experiments are intentionally leveraging grokking to unlock these breakthroughs at scale, offering notable business opportunities for enterprises seeking to improve AI model performance and reliability in high-stakes domains (source: God of Prompt, Twitter, Jan 6, 2026).

According to @godofprompt, achieving grokking in AI models—where a model transitions from memorization to generalization—depends on several critical factors: the use of weight decay (L2 regularization), data scarcity that pushes the model to discover true patterns, overparameterization to ensure sufficient capacity, prolonged training, and selecting the right optimizer, such as AdamW over SGD. Without these conditions, models tend to get stuck in memorization and fail to generalize, limiting their business value and practical applications in AI-driven analytics and automation (source: @godofprompt, Jan 6, 2026).