Curriculum learning is a training strategy in machine learning where models are trained on tasks of increasing difficulty. This approach mimics the way humans learn, starting with simpler concepts before progressing to more complex ones. By introducing easier tasks first, models can build foundational knowledge, which can enhance their performance on more challenging tasks later.

In practice, curriculum learning has been applied in various domains. For instance, in reinforcement learning, agents can be trained in simpler environments before facing more complex scenarios. This method has been shown to improve the efficiency of learning and the overall performance of the agents, as they are not overwhelmed by the complexity of the tasks from the outset ¹.

Additionally, curriculum learning has been successfully utilized in medical image analysis, where models are trained on images of varying difficulty based on annotator agreement. This approach has led to improved accuracy in classifying medical images compared to traditional training methods ².

Overall, curriculum learning is a powerful technique that can significantly enhance the learning process in various machine learning applications.