EP252: How batch sizes sharpen AI reasoning

Title: How Should LLMs Consume High-Quality Data? Optimal Data Scheduling via Quality-Aware Functional Scaling LawsSource: http://arxiv.org/abs/2605.25698v1Summary:This paper establishes foundational quality-aware functional scaling laws that provide the first theoretical closed-form solution for scheduling high-quality data during LLM training. The introduced 'Drop-Stable-Rampup' schedule optimizes training dynamics across noise-limited and signal-limited regimes, yielding significant breakthroughs in mathematical reasoning performance.