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arXiv · 2503.02318

Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models

Zhifei Xie·Mingbao Lin·Zihang Liu·Pengcheng Wu·Shuicheng Yan·Chunyan Miao
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Abstract

Recent advancements in multimodal reasoning have largely overlooked the audio modality. We introduce Audio-Reasoner, a large-scale audio language model for deep reasoning in audio tasks. We meticulously curated a large-scale and diverse multi-task audio dataset with simple annotations. Then, we leverage closed-source models to conduct secondary labeling, QA generation, along with structured COT process. These datasets together form a high-quality reasoning dataset with 1.2 million reasoning-rich samples, which we name CoTA. Following inference scaling principles, we train Audio-Reasoner on CoTA, enabling it to achieve great logical capabilities in audio reasoning. Experiments show state-of-the-art performance across key benchmarks, including MMAU-mini (+25.42%), AIR-Bench chat/foundation(+14.57%/+10.13%), and MELD (+8.01%). Our findings stress the core of structured CoT training in advancing audio reasoning.

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Zhifei Xie, Mingbao Lin, Zihang Liu, Pengcheng Wu, Shuicheng Yan, Chunyan Miao. 2025-03-04. Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models. https://arxiv.org/abs/2503.02318

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