全新音频问答模型 Omni-R1：利用文本驱动的强化学习和自动生成的数据推进音频问答
最近，一项来自 MIT CSAIL、哥廷根大学、IBM 研究所等机构的研究团队提出了一个名为 Omni-R1的全新音频问答模型。该模型在 Qwen2.5-Omni 的基础上，通过一种名为 GRPO（Group Relative Policy Optimization）的强化学习方法进行优化，显示出在音频问答任务中的出色表现。Omni-R1在著名的 MMAU 基准测试中创造了新的最先进成绩，涵盖了声音、语音和音乐等多个音频类别。研究团队指出，尽管模型的训练涉及音频数据，但其性能提升的主要原因竟然是文本推理能力的增强。这一发现让人惊讶，因为即使仅使用
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