Enterprise AI Analysis

An efficient model training framework for green Al

This paper introduces Play it Straight and its enhanced variant Re-Play it Straight, two adaptive training algorithms that combine random subset sampling with lightweight AL-inspired instance selection. The proposed framework achieves a better balance between accuracy and energy efficiency by incrementally fine-tuning models on small, informative subsets, while controlling computational overhead. Experiments on multiple benchmark datasets demonstrate substantial reductions in training energy compared to state-of-the-art DP and AL methods, with Re-Play it Straight consistently delivering superior performance. These results highlight the potential of our approach to support more sustainable deep learning practices, contributing to the broader goals of Green AI.