Revisiting the Scaling Properties of Downstream Metrics in Large Language Model Training

Researchers propose a new framework for predicting Large Language Model performance on downstream tasks directly from training budget, finding that simple power laws can accurately model scaling behavior. This challenges the traditional view that downstream task performance prediction is unreliable, offering better extrapolation than previous two-stage methods.

• →A direct framework can accurately model LLM benchmark performance scaling from training budget using power laws.
• →For fixed token-to-parameter ratios, log accuracy on downstream tasks follows predictable scaling patterns.
• →The direct approach provides better extrapolation than previously proposed two-stage procedures.
• →This research challenges the traditional view that downstream task performance prediction is unreliable.
• →The findings could improve resource allocation and planning for LLM training projects.