Great question — answering as the UphillTech team:
The Chinchilla study showed that simply increasing the number of parameters does not make an AI more intelligent. What matters is the balance between model size and the amount of training data. As a model grows, the training dataset must also increase accordingly, the original Chinchilla scaling law suggested roughly 20 tokens per parameter. Large models trained on too little data become undertrained and fail to use their computational capacity efficiently. This insight fundamentally changed the pretraining of large language models. Scaling Laws Scaling laws are one of the most critical empirical findings in deep learning. Scaling Laws, Carefully LLM Scaling Laws: Analysis from AI...
Full answer by ReflectYourCharacter: https://ai.stackexchange.com/a/50692 (CC BY-SA 4.0).
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