[Submitted on 18 Oct 2026]
Title:Beyond Policy Discrepancy: Capability-Aware Multi-Teacher On-Policy Distillation
View a PDF of the paper titled Beyond Policy Discrepancy: Capability-Aware Multi-Teacher On-Policy Distillation, by Xuanfeng Yang and 3 other authors
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Abstract:Multi-teacher on-policy distillation (MOPD) integrates domain specialists into a shared student, but determining how strongly each teacher should supervise remains challenging. Existing discrepancy-based allocation can assign strong supervision even when the student already matches or exceeds the teacher's task performance, since behavioral mismatch does not necessarily imply a remaining capability deficit. We propose Capability-Aware Multi-Teacher On-Policy Distillation (CA-MOPD), which combines specialist merging with capability-guided allocation to account for what the student has already learned. CA-MOPD first merges reinforcement-learning specialists into a strong student initialization, then uses verifier-estimated teacher--student capability gaps to adjust domain-level distillation strength. Supervision decreases as capability gaps close and increases when estimated student capability regresses, while dense token-level guidance remains provided by the teachers. Across six benchmarks spanning mathematics, code generation, and instruction following, CA-MOPD achieves an average score of 32.22, outperforming naive MOPD, Open-MOPD, and the merged initialization by 0.86, 0.83, and 0.74 points, respectively. Controlled comparisons further support the benefits of merged initialization and capability-gap guidance under matched training budgets.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.26950 [cs.LG]
(or arXiv:2610.26950v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.26950
arXiv-issued DOI via DataCite
Submission history
From: Xuanfeng Yang [view email] [v1] Sun, 18 Oct 2026 09:09:06 UTC (166 KB)
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