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Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting
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Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning
Code models strictly prioritize functional correctness, leaving software energy efficiency as an unoptimized byproduct. Training models to generate energy-efficient code requires reproducible feedback at scale, which physical hardware measurement cannot reliably provide due to variance.