Trust Functions Enable Near-Lossless Weak-to-Strong Model Generalization

Assigning scalar trust scores to weak labels and filtering training data with those scores allows a strong student model to match or exceed ground-truth supervised performance across world knowledge, quantitative reasoning, and strategy game domains. The method also supports iterative weak-to-strong chaining that compounds gains across training rounds.