X$^3$-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment
This work addresses the lack of high-quality audio reasoning data for large audio-language models, enabling them to perform deeper logical reasoning in auditory tasks.
X$^3$-OPD improves audio-language models' reasoning by distilling from a text teacher via on-policy alignment, achieving substantial gains on MMSU, MMAU, BIG Bench Audio, and MMAR benchmarks while preserving existing capabilities.
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 conditioned on its own acoustic perception, while the teacher provides token-level guidance using matched textual inputs and verified answers. We further construct a three-tier symmetric corpus covering textual reasoning rendered into speech, audio-event reasoning grounded in complex acoustic scenes, and spoken-dialogue reasoning involving paralinguistic cues. This design extends cross-modal distillation beyond textually recoverable content to reasoning grounded in non-linguistic events, prosody, and conversational context. Experiments on MMSU, MMAU, BIG Bench Audio, and MMAR demonstrate that X$^3$-OPD substantially improves audio-grounded reasoning and chain-of-thought quality while largely preserving the model's existing capabilities under domain shift.