Prescriptive Scaling Reveals the Evolution of Language Model Capabilities
This work provides a practical methodology for practitioners to translate compute budgets into reliable performance expectations and monitor shifts in capability boundaries over time, though it is incremental in refining scaling laws.
The study tackled the problem of predicting downstream accuracy from pre-training compute budgets for foundation models, finding that estimated capability boundaries are mostly stable across tasks except for math reasoning, which shows consistent advancement, and introduced an algorithm that recovers near-full data frontiers using about 20% of the evaluation budget.
For deploying foundation models, practitioners increasingly need prescriptive scaling laws: given a pre training compute budget, what downstream accuracy is attainable with contemporary post training practice, and how stable is that mapping as the field evolves? Using large scale observational evaluations with 5k observational and 2k newly sampled data on model performance, we estimate capability boundaries, high conditional quantiles of benchmark scores as a function of log pre training FLOPs, via smoothed quantile regression with a monotone, saturating sigmoid parameterization. We validate the temporal reliability by fitting on earlier model generations and evaluating on later releases. Across various tasks, the estimated boundaries are mostly stable, with the exception of math reasoning that exhibits a consistently advancing boundary over time. We then extend our approach to analyze task dependent saturation and to probe contamination related shifts on math reasoning tasks. Finally, we introduce an efficient algorithm that recovers near full data frontiers using roughly 20% of evaluation budget. Together, our work releases the Proteus 2k, the latest model performance evaluation dataset, and introduces a practical methodology for translating compute budgets into reliable performance expectations and for monitoring when capability boundaries shift across time.