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Life sciences · Preprint
arXiv · September 8, 2026
Posted before peer review. The findings may change or fail to hold.
This is a preprint introducing On-Policy Reverse Distillation (OPRD), a machine learning technique designed to enable weaker models to be used as supervisors for stronger student models without capping the student's performance. The authors report that OPRD achieves higher performance with fewer updates than existing RL and distillation baselines in successive model transfer and multi-teacher settings, though the work has not undergone peer review.
Preprint. Intervention: On-Policy Reverse Distillation (OPRD): a method that evaluates teacher policy shift relative to reference policy on student rollouts and amplifies verifier-driven policy gradient components along that direction. Compared with: Existing RL and distillation approaches, and verifier-based RL training alone.
OPRD achieves higher performance with fewer student updates than existing RL and distillation approaches in successive model transfer and multi-teacher distillation Response-style analysis shows OPRD students remain closer to models trained with verifier-based RL alone than to their weak teachers OPRD preserves stationary points of policy optimization while accelerating learning beyond the teacher capacity
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An unrefereed machine learning methods paper proposing a novel distillation algorithm with empirical results on policy optimization tasks, not yet peer reviewed.
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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 conventional distillation treats the weak teacher as an optimization target, potentially imposing its capacity ceiling on the student. We introduce On-Policy Reverse Distillation (OPRD), which evaluates the teacher's policy shift relative to its reference policy on student rollouts and amplifies the component of the student's verifier-driven policy gradient along that direction. By rescaling only verifier-supported updates, OPRD preserves the stationary points of policy optimization while accelerating learning beyond the teacher. In both successive model transfer and multi-teacher distillation, OPRD achieves higher performance with fewer student updates than existing RL and distillation approaches. Response-style analysis shows that OPRD students remain closer to models trained with verifier-based RL alone than to their weak teachers, suggesting that teacher guidance accelerates rather than redirects the student's own optimization. Results in conventional strong-to-weak distillation further demonstrate that OPRD effectively combines verifier-driven policy optimization with teacher guidance regardless of capacity ordering.
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