Omni-R1: Do You Really Need Audio to Fine-Tune Your Audio LLM?
Authors: name: Andrew Rouditchenko, name: Saurabhchand Bhati, name: Edson Araujo, name: Samuel Thomas, name: Hilde Kuehne, name: Rogerio Feris, name: James Glass

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.