Weak to Strong Generalization

用弱模型監督強模型，強模型真的能超越監督者嗎？OpenAI 實測發現，簡單微調只能恢復約一半的效能差距。透過置信度損失與引導策略，能將差距縮小至 20%，但仍有其邊界。本文深入拆解這份研究背後的機制與工程實踐。

When a weak model supervises a strong model, can the strong model truly surpass its supervisor? OpenAI’s experiments found that simple fine-tuning recovers only about half of the performance gap. With confidence loss and guidance strategies, the gap can shrink to around 20%, but boundaries remain. This article breaks down the mechanisms and engineering practice behind the study.