# Omni-R1: Do You Really Need Audio to Fine-Tune Your Audio LLM?

arXiv: https://arxiv.org/abs/2505.09439 · HTML: https://mlsanity.com/paper/2505.09439/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2025.

## Built on by (most cited first, 30 of 46)

- [Step-Audio 2 Technical Report](https://mlsanity.com/paper/2507.16632/) (2025)
- [Audio-Thinker: Guiding Audio Language Model When and How to Think via Reinforcement Learning](https://mlsanity.com/paper/2508.08039/) (2025)
- [Audio Flamingo Next: Next-Generation Open Audio-Language Models for Speech, Sound, and Music](https://mlsanity.com/paper/2604.10905/) (2026)
- [UALM: Unified Audio Language Model for Understanding, Generation and Reasoning](https://mlsanity.com/paper/2510.12000/) (2025)
- [ThinkOmni: Lifting Textual Reasoning to Omni-modal Scenarios via Guidance Decoding](https://mlsanity.com/paper/2602.23306/) (2026)
- [Exploring Fine-Tuning of Large Audio Language Models for Spoken Language Understanding under Limited Speech Data](https://mlsanity.com/paper/2509.15389/) (2025)
- [Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel](https://mlsanity.com/paper/2509.25913/) (2025)
- [Towards Audio Token Compression in Large Audio Language Models](https://mlsanity.com/paper/2511.20973/) (2025)
- [The Interspeech 2026 Audio Reasoning Challenge: Evaluating Reasoning Process Quality for Audio Reasoning Models and Agents](https://mlsanity.com/paper/2602.14224/) (2026)
- [Pay More Attention To Audio: Mitigating Imbalance of Cross-Modal Attention in Large Audio Language Models](https://mlsanity.com/paper/2509.18816/) (2025)
- [Omni-AutoThink: Adaptive Multimodal Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2512.03783/) (2025)
- [Advancing Speech Understanding in Speech-Aware Language Models with GRPO](https://mlsanity.com/paper/2509.16990/) (2025)
- [LLaSO: A Foundational Framework for Reproducible Research in Large Language and Speech Model](https://mlsanity.com/paper/2508.15418/) (2025)
- [Echo: Towards Advanced Audio Comprehension via Audio-Interleaved Reasoning](https://mlsanity.com/paper/2602.11909/) (2026)
- [ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning](https://mlsanity.com/paper/2604.07851/) (2026)
- [Escape the Language Prior: Mitigating Late-Stage Modality Collapse in Audio Reasoning via Modality-Aware Policy Optimization](https://mlsanity.com/paper/2605.27741/) (2026)
- [Reinforcement Learning with Evolving Rubrics as Rewards for Audio Reasoning](https://mlsanity.com/paper/2608.02831/) (2026)
- [Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio question answering](https://mlsanity.com/paper/2509.12275/) (2025)
- [Long Grounded Thoughts: Synthesizing Visual Problems and Reasoning Chains at Scale](https://mlsanity.com/paper/2511.05705/) (2025)
- [Learning When to Think While Listening in Large Audio-Language Models](https://mlsanity.com/paper/2605.27190/) (2026)
- [A Closer Look at Failure Modes in Temporal Understanding of Large Audio-Language Models](https://mlsanity.com/paper/2606.17417/) (2026)
- [EvoAudio: Recursive Self-Improvement for Audio Understanding](https://mlsanity.com/paper/2609.27389/) (2026)
- [SightSound-R1: Cross-Modal Reasoning Distillation from Vision to Audio Language Models](https://mlsanity.com/paper/2509.15661/) (2025)
- [Think Smart, Not Hard: Difficulty Adaptive Reasoning for Large Audio Language Models](https://mlsanity.com/paper/2509.21960/) (2025)
- [Rebellion: Noise-Robust Reasoning Training for Audio Reasoning Models](https://mlsanity.com/paper/2511.09682/) (2025)
- [When Scaling Fails: Mitigating Audio Perception Decay of LALMs via Multi-Step Perception-Aware Reasoning](https://mlsanity.com/paper/2603.02266/) (2026)
- [Words at Play: Benchmarking Audio Pun Understanding in Large Audio-Language Models](https://mlsanity.com/paper/2603.18678/) (2026)
- [Audio-DeepThinker: Progressive Reasoning-Aware Reinforcement Learning for High-Quality Chain-of-Thought Emergence in Audio Language Models](https://mlsanity.com/paper/2604.18187/) (2026)
- [Omni-Persona: Systematic Benchmarking and Improving Omnimodal Personalization](https://mlsanity.com/paper/2605.09996/) (2026)
- [RAIL: Rethinking Auditory Intelligence in Large Audio-Language Models with a CHC-Grounded Benchmark](https://mlsanity.com/paper/2606.11260/) (2026)

## Abstract (arXiv)

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 and MMAR benchmarks. 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.