Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillation
Abstract
Multi-teacher on-policy distillation (MOPD) extends on-policy distillation (OPD) by routing domain-specific data to specialized teachers, integrating their capabilities through dense token-level supervision on student-generated trajectories. However, domain routing only distinguishes the supervision sources for different domains; updates from all domains still act on shared parameters. When teachers differ substantially in response style and domain expertise, supervision that is effective in isolation may still lead to persistent gradient conflicts and capability degradation during joint optimization. To address this challenge, we propose Cross-Teacher Guarding for MOPD (Road MOPD), which introduces non-routed teachers as auxiliary cross-domain critics on the same student trajectories and coordinates multi-teacher supervision through token-level credit assignment and gradient updates. Specifically, cross-teacher-guided credit assignment uses teacher agreement to strengthen token-level corrections jointly supported by the teachers and attenuate conflicting signals; preventive multi-teacher gradient projection uses auxiliary gradients from the same trajectories to constrain potentially conflicting updates and adaptively controls projection strength to retain target-domain learning signals. In experiments, Road MOPD mitigates degradation in the disadvantaged domain, gains over 5 percentage points under high conflict, and improves overall performance in low-conflict and three-domain settings.
Community
Developed jointly by JD.com’s OxygenVLM team and Nanjing University, RoadMOPD mitigates learning conflicts among teachers with substantially different response styles and domain expertise, helping a student integrate multiple experts’ capabilities while reducing degradation in individual domains. It brings routed and auxiliary teachers together to evaluate the same student-generated response, using teacher agreement to strengthen jointly supported token-level corrections and attenuate conflicting signals. Preventive gradient projection then suppresses conflicting update components while adaptively preserving the target domain’s learning signal. With Qwen3-4B, RoadMOPD improves both domains over Vanilla MOPD in the high-conflict Math–Medical and Code–Medical settings, boosting average medical accuracy by up to 8.78 percentage points. Benefits also extend to the more compatible Math–Code combination, with improvements across all three domains under joint Math–Code–Medical training. The code is currently undergoing internal company approval for open-source release and is expected to be released within the next 1–2 months.
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