Easy to Hard Generalization

Easy-to-hard generalization in machine learning focuses on training models to perform well on complex tasks using only simpler, easier-to-label training data. Current research investigates this phenomenon across various architectures, including large language models (LLMs) and convolutional neural networks (CNNs), exploring techniques like multi-teacher distillation, instruction-based editing, and prompting strategies such as least-to-most prompting to improve generalization. This research is crucial for advancing AI capabilities beyond human-level supervision and for developing more efficient and robust machine learning systems across diverse applications. The ability to generalize from easy to hard tasks directly impacts the scalability and cost-effectiveness of training advanced AI models.