X$^3$-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment

While large audio-language models have achieved remarkable progress in auditory perception, they still lag behind text-based large language models in deep logical reasoning, primarily due to the scarcity of high-quality audio reasoning data. To bridge this gap, we propose X$^3$-OPD, a cross-modal on-policy distillation framework that transfers reasoning capabilities from a powerful text teacher to an audio-language student. During training, the student generates reasoning trajectories conditione

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ethics.ai (23 July 2026), “X$^3$-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment,” evidence record 13498, https://ethics.ai/record/13498 (originally published by arXiv cs.LG).

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