Eliciting Weak-to-Strong Generalization with On-Policy Reverse Distillation
Weak-to-strong generalization asks whether stronger models can learn from weaker supervisors and surpass them. This question is particularly important for successive model generations and multi-domain consolidation, where repeating frontier-scale post-training from scratch can be prohibitively expensive. Yet convention...
Youngrok Park, Sangmin Bae, Hojung Jung et al.
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