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[2602.15327] Prescriptive Scaling Reveals the Evolution of Language Model Capabilities
Summary
The paper discusses prescriptive scaling laws for language models, focusing on how compute budgets affect downstream accuracy and the stability of these mappings over time.
Why It Matters
Understanding prescriptive scaling is crucial for practitioners in AI and machine learning, as it helps translate compute resources into expected model performance. This research provides insights into the evolving capabilities of language models, which can inform future developments and applications in the field.
Key Takeaways
- Prescriptive scaling laws help predict model performance based on compute budgets.
- The study reveals mostly stable capability boundaries for various tasks, except in math reasoning.
- An efficient algorithm is introduced to recover data frontiers using minimal evaluation budgets.
- The research releases the Proteus 2k dataset for model performance evaluation.
- Temporal reliability of scaling laws is validated across different model generations.
Computer Science > Machine Learning arXiv:2602.15327 (cs) [Submitted on 17 Feb 2026] Title:Prescriptive Scaling Reveals the Evolution of Language Model Capabilities Authors:Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis, Sham Kakade View a PDF of the paper titled Prescriptive Scaling Reveals the Evolution of Language Model Capabilities, by Hanlin Zhang and 3 other authors View PDF HTML (experimental) Abstract: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 roughl...
