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R-OPD, a gradient-triggered curriculum that adaptively selects when to revisit initial-student trajectories, improves average accuracy over continued current-policy sampling and produces longer responses, suggesting that well-timed revisits help students use more of their reasoning capacity.
The results show that the key to memory-augmented repair is not simply accumulating more experiences, but retrieving the right experiences for the right repair context, and that relevance, quality, and redundancy matter more than raw memory volume.
RouteOPD (Routed On-Policy Distillation), which decomposes local teacher--student disagreement into student-excess sources and teacher-deficit destinations and couples them into explicit transport pairs and demonstrates the effectiveness of explicitly modeling probability transport in on-policy distillation.
It is shown the contamination is structurally irreversible: removing a source skill after the fact cannot erase the flawed reasoning its descendants have already inherited, so post-hoc rollback recovers only a small fraction of the lost performance, making skill admission a pre-commit necessity rather than a post-hoc f...
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