A technique used in machine learning to improve model performance over time is reinforcement learning. This approach involves training algorithms through trial and error, where models receive rewards or penalties based on their actions in a given environment. By learning from past interactions, the model progressively improves its decision-making capabilities. As it encounters new data and situations, reinforcement learning enables adaptive learning, ultimately resulting in highly refined and responsive models suitable for various applications across industries, from gaming to robotics.