Curriculum Learning

Curriculum learning (CL) is a machine learning technique that improves model training by presenting data in a progressively increasing order of difficulty, mimicking human learning. Current research focuses on automating curriculum design using various methods, including Fisher information, temporal difference errors, and LLM-generated difficulty metrics, applied across diverse model architectures like large language models (LLMs), graph neural networks (GNNs), and reinforcement learning (RL) agents. CL's significance lies in its ability to enhance model efficiency, generalization, and robustness across numerous applications, including natural language processing, robotics, and computer vision, by reducing training time and improving performance on complex tasks.