全新音頻問答模型 Omni-R1：利用文本驅動的強化學習和自動生成的數據推進音頻問答
最近，一項來自 MIT CSAIL、哥廷根大學、IBM 研究所等機構的研究團隊提出了一個名爲 Omni-R1的全新音頻問答模型。該模型在 Qwen2.5-Omni 的基礎上，通過一種名爲 GRPO（Group Relative Policy Optimization）的強化學習方法進行優化，顯示出在音頻問答任務中的出色表現。Omni-R1在著名的 MMAU 基準測試中創造了新的最先進成績，涵蓋了聲音、語音和音樂等多個音頻類別。研究團隊指出，儘管模型的訓練涉及音頻數據，但其性能提升的主要原因竟然是文本推理能力的增強。這一發現讓人驚訝，因爲即使僅使用
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