Jordan Hoffmann and the DeepMind research team published a landmark paper introducing the Chinchilla scaling laws. They mathematically proved that large language models were heavily overparameterized and undertrained, demonstrating that keeping models smaller while massively expanding the volume of training text yielded superior intelligence and cost-efficiency.
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