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Akshay Ratnawat on Instagram: "At LLM scale, weight decay is NOT regularization. Most candidates answer: - “It’s a regularizer” - “It prevents overfitting” - “It improves generalization” But given the parameters size and data size it’s very difficult to overfit LLMs. Hence overfitting is not the problem. 🛠️ The solution (what’s actually happening) At LLM scale, weight decay is a control knob. For AdamW / SGD with weight decay, the update is effectively: w(t+1)=w(t)−η∇L(w(t))−ηλw(t) Weight decay
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