AINeutralarXiv – CS AI · Jun 96/10
🧠
Revisiting Training Scale: An Empirical Study of Token Count, Power Consumption, and Parameter Efficiency
A new empirical study challenges the assumption that scaling training token counts linearly improves large language model performance, revealing instead that increased token counts lead to strictly declining training efficiency when energy consumption and execution duration are measured alongside traditional metrics.
