This academic paper introduces "minimum attention" as a novel regularization technique applied to meta-reinforcement learning (meta-RL), particularly within model-based RL frameworks. The authors integrate this principle, which penalizes large changes in control over state and time, into the reward function to enhance learning. They demonstrate empirically that this approach improves fast adaptation in new tasks and reduces variance during training, suggesting its potential for safer and more energy-efficient policy learning in high-dimensional robotic control systems like HalfCheetah, Hopper, and Walker2D. The study compares its performance against state-of-the-art model-free and model-based RL algorithms, highlighting its benefits in generalization and robustness to environmental and model perturbations.