arXiv cs.CLPaper
Eliciting Weak-to-Strong Generalization with On-Policy Reverse Distillation
This addresses a real scaling problem: how to post-train new model generations without re-annotating everything from scratch. Instead of copying the weak teacher's outputs, OPRD rescales the student's own gradients along directions the weak teacher is moving, preserving the student's potential to exceed it. Useful for multi-generation transfer and multi-teacher consolidation when annotation budget is tight.