title: Omni-R1: Do You Really Need Audio to Fine-Tune Your Audio LLM?
summary: We propose Omni-R1 which fine-tunes a recent multi-modal LLM, Qwen2.5-Omni,
  on an audio question answering dataset with the reinforcement learning method
  GRPO. This leads to new State-of-the-Art performance on the recent MMAU
  benchmark. Omni-R1 achieves the highest accuracies on the sounds, music,
  speech, and overall average categories, both on the Test-mini and Test-full
  splits. To understand the performance improvement, we tested models both with
  and without audio and found that much of the performance improvement from GRPO
  could be attributed to better text-based reasoning. We also made a surprising
  discovery that fine-tuning without audio on a text-only dataset was effective
  at improving the audio-based performance.
paper_authors._id: 6825cfe4658e0204a1b492c7
paper_authors.name: Andrew Rouditchenko
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-15T11:29:56.341Z
paper_authors._id: 6253482bde8d23c3ce8816d0
paper_authors.avatarUrl: /avatars/44475ac0e514a4e88d148ab43d0fd489.svg
paper_authors.fullname: Andrew Rouditchenko
paper_authors.type: user
paper_authors.user: h9LtLSb
paper_authors._id: 6825cfe4658e0204a1b492c8
paper_authors.name: Saurabhchand Bhati
paper_authors._id: 6825cfe4658e0204a1b492c9
paper_authors.name: Edson Araujo
paper_authors._id: 6825cfe4658e0204a1b492ca
paper_authors.name: Samuel Thomas
paper_authors._id: 6825cfe4658e0204a1b492cb
paper_authors.name: Hilde Kuehne
paper_authors._id: 6825cfe4658e0204a1b492cc
paper_authors.name: Rogerio Feris
paper_authors._id: 6825cfe4658e0204a1b492cd
paper_authors.name: James Glass
paper_title: Omni-R1: Do You Really Need Audio to Fine-Tune Your Audio LLM?
paper_summary: We propose Omni-R1 which fine-tunes a recent multi-modal LLM, Qwen2.5-Omni,
  on an audio question answering dataset with the reinforcement learning method
  GRPO. This leads to new State-of-the-Art performance on the recent MMAU
  benchmark. Omni-R1 achieves the highest accuracies on the sounds, music,
  speech, and overall average categories, both on the Test-mini and Test-full
  splits. To understand the performance improvement, we tested models both with
  and without audio and found that much of the performance improvement from GRPO
  could be attributed to better text-based reasoning. We also made a surprising
  discovery that fine-tuning without audio on a text-only dataset was effective
  at improving the audio-based performance.
paper_ai_summary: Omni-R1 fine-tunes Qwen2.5-Omni with GRPO on an audio QA dataset, achieving state-of-the-art performance in sound, music, speech, and average categories.
paper_ai_keywords: multi-modal LLM Qwen2.5-Omni audio question answering reinforcement learning GRPO MMAU benchmark text-based reasoning