Weak to Strong

Weak-to-strong (W2S) generalization explores how a powerful model ("strong student") can learn effectively from a less capable model ("weak teacher"), even when the teacher's guidance is imperfect or incomplete. Current research focuses on improving W2S learning in various contexts, including large language models (LLMs) and other machine learning architectures, often employing ensemble methods, self-training, and adaptive weighting schemes to mitigate the impact of noisy or limited teacher supervision. This research area is significant because it addresses the challenge of efficiently training and aligning increasingly complex AI systems, particularly in scenarios where obtaining high-quality labeled data is difficult or expensive, and has implications for improving the reliability and safety of advanced AI.