Audio-R1: Audio Reasoning through Reinforcement Learning on Self-Supervised Tasks

Audio-R1 is a reinforcement learning post‑training framework that leverages audio self‑supervised learning tasks to provide verifiable rewards for large audio‑language models. By automatically determining answers during task construction, Audio‑R1 verifies model responses and computes rewards, leading to consistent performance gains across multiple models and audio benchmarks. Ablation studies show each self‑supervised task contributes to the improvement, and joint training yields additional benefits, especially on downstream tasks distinct from the training tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• reinforcement-learning
• fine-tuning
• multimodal
• benchmarks