An Active-Bottleneck Mechanism for Weak-to-Strong Generalization
Weak-to-strong generalization (W2SG) occurs when a student trained on a teacher's predictions outperforms that teacher. We study when this happens under fully converged, ridgeless two-stage learning, with no early stopping, no explicit regularization, and no assumption that the student is more expressive than the teacher. In two-stage linear regression, a teacher is fit from $n$ labeled examples and a student is trained solely on the teacher's predictions on $m$ fresh, unlabeled inputs. Although both stages share the same hypothesis class and the same training rule, we show that the student outperforms the teacher exactly when $m$ lies in an explicit intermediate range: too few pseudo-lab...
| Authors | Mohammad Zeinalpour, Amir Najafi |
|---|---|
| Primary category | stat.ML |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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