Revisiting the Scaling Properties of Downstream Metrics in Large Language Model Training
Apple Machine Learning Journal / 3/26/2026
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Key Points
- The paper revisits how downstream evaluation metrics scale during large language model training, aiming to better characterize their relationship to training progress and compute/data scaling.
- It analyzes whether downstream metric improvements follow predictable scaling laws, and under what conditions those properties may change or break.
- The study focuses on implications for interpreting training runs and forecasting downstream performance from intermediate results.
- The authors present findings intended to inform more reliable evaluation practices and scaling expectations when developing and tuning LLMs.
While scaling laws for Large Language Models (LLMs) traditionally focus on proxy metrics like pretraining loss, predicting downstream task performance has been considered unreliable. This paper challenges that view by proposing a direct framework to model the scaling of benchmark performance from the training budget. We find that for a fixed token-to-parameter ratio, a simple power law can accurately describe the scaling behavior of log accuracy on multiple popular downstream tasks. Our results show that the direct approach extrapolates better than the previously proposed two-stage procedure…
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