@inproceedings{fu-etal-2026-x3,
title = "{X}$^3$-{OPD}: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment",
author = "Fu, Dongjie and
Cao, Di and
Zhang, Zihan and
Jia, Wenxu and
Chen, Yifu and
Ji, Shengpeng and
Zhang, Yu and
Cheng, Xize and
Jin, Tao",
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.1917/",
pages = "43674--43687",
ISBN = "979-8-89176-459-0",
abstract = "While audio large language models have achieved remarkable progress in auditory perception, they still lag behind text-based large language models in deep logical reasoning capabilities. This performance disparity primarily stems from the scarcity of high-quality audio reasoning data. To bridge this modality gap, we propose a novel cross-modal on-policy distillation framework. Our approach leverages a powerful text model as a teacher to provide dynamic correction and guidance to the audio foundational model during on-policy exploration. Furthermore, we construct a three-tier symmetric dataset encompassing logical reasoning, complex audio reasoning, and speech dialogue. By dynamically aligning the student model{'}s reasoning trajectories within the high-noise auditory space with the teacher model{'}s prior knowledge in the clean text space, our method effectively distills robust reasoning capabilities into the audio domain. Extensive experiments demonstrate that our model achieves substantial improvements on the audio and speech understanding benchmarks, paving a new path for comprehensive audio-language understanding."
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<abstract>While audio large language models have achieved remarkable progress in auditory perception, they still lag behind text-based large language models in deep logical reasoning capabilities. This performance disparity primarily stems from the scarcity of high-quality audio reasoning data. To bridge this modality gap, we propose a novel cross-modal on-policy distillation framework. Our approach leverages a powerful text model as a teacher to provide dynamic correction and guidance to the audio foundational model during on-policy exploration. Furthermore, we construct a three-tier symmetric dataset encompassing logical reasoning, complex audio reasoning, and speech dialogue. By dynamically aligning the student model’s reasoning trajectories within the high-noise auditory space with the teacher model’s prior knowledge in the clean text space, our method effectively distills robust reasoning capabilities into the audio domain. Extensive experiments demonstrate that our model achieves substantial improvements on the audio and speech understanding benchmarks, paving a new path for comprehensive audio-language understanding.</abstract>
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%0 Conference Proceedings
%T X³-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment
%A Fu, Dongjie
%A Cao, Di
%A Zhang, Zihan
%A Jia, Wenxu
%A Chen, Yifu
%A Ji, Shengpeng
%A Zhang, Yu
%A Cheng, Xize
%A Jin, Tao
%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 fu-etal-2026-x3
%X While audio large language models have achieved remarkable progress in auditory perception, they still lag behind text-based large language models in deep logical reasoning capabilities. This performance disparity primarily stems from the scarcity of high-quality audio reasoning data. To bridge this modality gap, we propose a novel cross-modal on-policy distillation framework. Our approach leverages a powerful text model as a teacher to provide dynamic correction and guidance to the audio foundational model during on-policy exploration. Furthermore, we construct a three-tier symmetric dataset encompassing logical reasoning, complex audio reasoning, and speech dialogue. By dynamically aligning the student model’s reasoning trajectories within the high-noise auditory space with the teacher model’s prior knowledge in the clean text space, our method effectively distills robust reasoning capabilities into the audio domain. Extensive experiments demonstrate that our model achieves substantial improvements on the audio and speech understanding benchmarks, paving a new path for comprehensive audio-language understanding.
%U https://aclanthology.org/2026.emnlp-main.1917/
%P 43674-43687
Markdown (Informal)
[X3-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment](https://aclanthology.org/2026.emnlp-main.1917/) (Fu et al., EMNLP 2026)
ACL
- Dongjie Fu, Di Cao, Zihan Zhang, Wenxu Jia, Yifu Chen, Shengpeng Ji, Yu Zhang, Xize Cheng, and Tao Jin. 2026. X3-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 43674–43687, Budapest, Hungary. Association for Computational Linguistics.