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Compute-Data (CD) scaling laws are proposed, a unified framework that bridges compute-optimal scaling, where data scales freely with compute, and data-optimal scaling, where the corpus is fixed while compute can grow without bound.
MatrAIx is introduced, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users and provides an end-to-end infrastructure for evaluating AI systems and digital products with diverse simulated human users.
This work formalize and quantify data synergy in language model pretraining by leveraging observational variation across open-weight LLMs with diverse pretraining mixtures and estimating both direct domain-to-benchmark synergy and second-order domain-domain synergy (capabilities that require co-occurrence of multiple domains).
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