arXiv: cs.AI preprintsInternational6 November 2026
P$^2$-MOPD: Accelerating Multi-Teacher On-Policy Distillation via Parameter Priors
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arXiv:2611.03953v2 Announce Type: replace-cross Abstract: Multi-teacher on-policy distillation consolidates expert capabilities by supervising student-generated trajectories, whereas task arithmetic offers a training-free alternative through linear parameter combinations. We view the linear combination of student and expert parameters as a single differentiable model and propose P$^2$-MOPD, which jointly optimizes the mixing coefficients and the student weights under the same on-policy distillation objective. Unlike task arithmetic, which stays within the span of the frozen student and expert
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