Machine LearningAlignment
Weak-to-Strong Generalization
Investigating whether weak supervision can elicit strong capabilities in models.
The Problem
The classic teacher-student model in machine learning posits that a strong teacher supervises a weak student. But what happens when the teacher is weaker than the student? This is the core of the Weak-to-Strong Generalization problem.
Related Papers
2025International Conference on Learning Representations (ICLR), Apr, 2025
Theoretical Findings
We theoretically investigate this for binary and multilabel classification. We identify two asymptotic phases: successful generalization and random guessing.
Key Insight
"Key Insight: The student can generalize even with random-guessing supervision under certain conditions."