SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models

• URL: http://arxiv.org/abs/2506.12935v1
• Date: Sun, 15 Jun 2025 18:26:08 GMT
• Title: SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models
• Authors: Xingjian Diao, Chunhui Zhang, Keyi Kong, Weiyi Wu, Chiyu Ma, Zhongyu Ouyang, Peijun Qing, Soroush Vosoughi, Jiang Gui,
• Abstract summary: We introduce the Audio Logical Reasoning dataset, consisting of 6,446 text-audio annotated samples.<n>We then propose SoundMind, a rule-based reinforcement learning algorithm tailored to endow ALMs with deep bimodal reasoning abilities.<n>Our approach achieves state-of-the-art performance in audio logical reasoning.
• Score: 25.143840124269193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While large language models have shown reasoning capabilities, their application to the audio modality, particularly in large audio-language models (ALMs), remains significantly underdeveloped. Addressing this gap requires a systematic approach, involving a capable base model, high-quality reasoning-oriented audio data, and effective training algorithms. In this study, we present a comprehensive solution: we introduce the Audio Logical Reasoning (ALR) dataset, consisting of 6,446 text-audio annotated samples specifically designed for complex reasoning tasks. Building on this resource, we propose SoundMind, a rule-based reinforcement learning (RL) algorithm tailored to endow ALMs with deep bimodal reasoning abilities. By training Qwen2.5-Omni-7B on the ALR dataset using SoundMind, our approach achieves state-of-the-art performance in audio logical reasoning. This work highlights the impact of combining high-quality, reasoning-focused datasets with specialized RL techniques, advancing the frontier of auditory intelligence in language models. Our code and the proposed dataset are available at https://github.com/xid32/SoundMind.

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