The Mechanism of Weak-to-Strong Generalization: Feature Elicitation from Latent Knowledge
Weak-to-strong (W2S) generalization, in which a strong model is fine-tuned on outputs of a weaker, task-specialized model, has been proposed as an approach to aligning superhuman AI systems. Existing theoretical analyses either fix the student's representations or operate in restricted settings. Whether multi-step SGD can succeed in feature learning while preserving diverse pre-trained capabilities remains open. We study W2S in the setting of reward-model learning with two-layer neural networks. The strong model has pre-trained representations organized into low-dimensional subspaces $V_k$, and is fine-tuned under the supervision of a weak model specialized on task $κ$. We prove that the ...
| Authors | Ryoya Awano, Taiji Suzuki |
|---|---|
| Primary category | stat.ML |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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