A recent study proposes using reinforcement learning to optimize the power consumption of large language models (LLMs) during training. By conducting experiments on single and multiple NVIDIA A100 GPUs, the researchers developed a reinforcement learning-based controller that adjusts the workload parameters based on measured power usage. The results show that this controller reduces power limit violations by 89.8% while increasing token output by 18.1% and energy efficiency by 26.2%. In the 72B model experiment, the controller achieved a 35.7% increase in output across three replications, with a 87.2% reduction in power limit violations. Furthermore, the researchers found that as the measurement window increases, the number of power limit violations decreases. In a 16-GPU fleet, power limit violations were zero at 30 seconds and above. Blogger's Review: Using reinforcement learning to optimize AI datacenter power consumption is a very promising direction. This study demonstrates that by measuring power usage and adjusting workload parameters, power limit violations can be effectively reduced and energy efficiency improved. This approach not only reduces datacenter power consumption but also decreases carbon emissions, having significant economic and environmental implications.