@inproceedings{yin-etal-2026-h,
title = "{H}-{OPD}: Confidence Aware Heterogeneous Multi-Teacher Multimodal On-Policy Distillation",
author = "Yin, Qixiang and
Yao, Huanjin and
Yuchen, Cai and
Chen, Jianghao and
Wang, Ziyi and
Yang, Min and
Su, Fei and
Zhao, Zhicheng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.208/",
pages = "4780--4797",
ISBN = "979-8-89176-459-0",
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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<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.</abstract>
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%0 Conference Proceedings
%T H-OPD: Confidence Aware Heterogeneous Multi-Teacher Multimodal On-Policy Distillation
%A Yin, Qixiang
%A Yao, Huanjin
%A Yuchen, Cai
%A Chen, Jianghao
%A Wang, Ziyi
%A Yang, Min
%A Su, Fei
%A Zhao, Zhicheng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yin-etal-2026-h
%X 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.
%U https://aclanthology.org/2026.emnlp-main.208/
%P 4780-4797
Markdown (Informal)
[H-OPD: Confidence Aware Heterogeneous Multi-Teacher Multimodal On-Policy Distillation](https://aclanthology.org/2026.emnlp-main.208/) (Yin et al., EMNLP 2026)
ACL
- Qixiang Yin, Huanjin Yao, Cai Yuchen, Jianghao Chen, Ziyi Wang, Min Yang, Fei Su, and Zhicheng Zhao. 2026. H-OPD: Confidence Aware Heterogeneous Multi-Teacher Multimodal On-Policy Distillation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 4780–4797, Budapest, Hungary. Association for Computational Linguistics.