Grokking: Generalization beyond Overfitting on small algorithmic datasets (Paper Explained)
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AI • Machine Learning • Tech on Instagram: "Grokking in large language models (LLMs) refers to a phase where a model obtains a deep, general understanding of a task after extended training. Before grokking, a model may perform well on training examples but fail to generalize; after grokking, it starts solving new cases correctly with more training data. In LLMs, signs of real understanding include stable performance on out-of-distribution data, consistent reasoning across varied prompts, and rob
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Code.hub on Instagram: "Grokking 🧠 in AI is the moment when a model stops memorizing patterns and truly starts understanding them. . Early in training 📉, loss drops fast because the model fits surface-level data without real generalization. . Inside the network ⚙️, activations look messy — showing the model hasn’t formed structured knowledge yet. . After long training ⏳, representations suddenly reorganize and test accuracy jumps sharply. . This delayed breakthrough 🚀 is called grokking — whe
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