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One Teacher, Many Views: Reducing Reference Sensitivity in On-Policy Self-Distillation
On-policy self-distillation (OPSD) improves reasoning by training a model on its own trajectories with dense token-level supervision from a privileged copy conditioned on a reference solution. Existing methods typically use a single teacher distribution conditioned on the complete reference. We iden
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