
Weak-To-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision
AI Safety Fundamentals: Alignment
Exploring Weak to Strong Generalization in AI Alignment
The chapter delves into the challenge of aligning superhuman models with human supervision by proposing a setup where weak models supervise strong models, addressing the weak-to-strong learning problem. It discusses fine-tuning large, pre-trained models with labels from weaker models and explores the concept of weak-to-strong generalization in AI alignment, focusing on fine-tuning GPT-4 models. The chapter highlights both the limitations and potential of the approach, emphasizing the importance of continuous improvement in aligning superhuman models and the advantages of weak supervision in training AI models.
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