arXiv:2602.15327v2 Announce Type: replace Abstract: Machine learning model performance improvements tend to arise from competition and application. For deployment, we consider 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 existing and 2k newly evaluated model checkpoints spanning 2022-2026 across six benchmarks, we estimate capability boundaries, high conditional quantiles of benchmark scores
The proliferation of language models and increasing compute budgets necessitate a clearer understanding of scaling laws, moving beyond theoretical benchmarks to practical, prescriptive guidance for deployment.
This research provides a framework for predicting and optimizing Language Model capabilities based on compute investment, offering critical insights for strategic planning in AI development and deployment.
The ability to more accurately predict downstream accuracy from pre-training compute budgets changes how companies and nations will plan their AI investments and development strategies.
- · AI developers
- · Cloud providers
- · AI-first companies
- · Researchers specializing in ML observability
- · Companies with inefficient compute allocation strategies
- · Projects relying solely on reactive model development
- · Legacy AI solutions
Optimized allocation of significant compute resources towards Language Model development.
Increased efficiency in AI model training, potentially accelerating the development of more advanced AI systems.
Enhanced predictability in AI investment may lead to more concentrated and effective national AI strategies.
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Read at arXiv cs.LG