Concept

Power-law Reduction Phase in LLM Scaling

Following the initial slow improvement, a model enters the power-law reduction phase. In this stage, test errors decrease significantly and predictably as the training dataset size increases. On a log-log plot, this relationship manifests as a steep, linear decline, indicating that scaling up data is highly effective and follows a power-law.

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Updated 2026-05-02

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Ch.2 Generative Models - Foundations of Large Language Models

Foundations of Large Language Models

Foundations of Large Language Models Course

Computing Sciences