How are meta-learning algorithms used to enable models to learn to learn?

Meta-learning algorithms, also known as learning to learn algorithms, train models to learn new tasks with minimal data by leveraging knowledge gained from previous tasks. These algorithms typically involve training a meta-learner on a distribution of tasks, allowing it to adapt quickly to new tasks with limited samples. Meta-learning has applications in few-shot learning, transfer learning, and adaptive optimization.