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Core
Scaling Laws
Scaling laws say model loss drops predictably as a power law in parameters, data, and compute, which is why bigger models trained on more data reliably improve. The Chinchilla result showed that for a fixed compute budget you should scale parameters and training tokens together (roughly equally), meaning prior large models were under-trained. This reshaped how compute is allocated and why smaller, data-heavy models hold up. AI, ML, and GenAI interviews probe it because it underlies model-selection and the data-vs-size economics.
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