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arXiv · 2607.02592

H-OPD: Confidence Aware Heterogeneous Multi-Teacher Multimodal On-policy Distillation

Qixiang Yin·Huanjin Yao·Yuchen Cai·Jianghao Chen·Ziyi Wang·Min Yang·Fei Su·Zhicheng Zhao
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Abstract

On-policy distillation (OPD) has recently emerged as an effective post-training paradigm by providing supervision on student-generated trajectories. However, existing OPD methods for multimodal reasoning usually rely on a static teacher routing, assigning each sample to a single teacher based on modality or task type. This ignores that visual grounding and abstract reasoning may dominate different decoding steps, making a single teacher insufficient for the full trajectory. To this end, H-OPD is proposed as a confidence-aware heterogeneous multi-teacher OPD framework for multimodal reasoning. By verifying the complementarity of heterogeneous teachers in the same reasoning process, H-OPD replaces task or sample level teacher routing with token-level teacher arbitration along the shared student trajectory. H-OPD employs vision-to-language description transfer to enable text-only teachers to access key visual semantics, and uses a confidence-aware arbitration mechanism to dynamically combine vision-language teacher and text-only teachers at each token. Extensive evaluations over 11 widely-used reasoning benchmarks showcase the superior performance of our method.

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Qixiang Yin, Huanjin Yao, Yuchen Cai, Jianghao Chen, Ziyi Wang, Min Yang, Fei Su, Zhicheng Zhao. 2026-07-01. H-OPD: Confidence Aware Heterogeneous Multi-Teacher Multimodal On-policy Distillation. https://arxiv.org/abs/2607.02592

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