AudioRouter: Data Efficient Audio Understanding via RL based Dual Reasoning

Researchers have developed AudioRouter, a reinforcement learning framework that teaches Large Audio Language Models (LALMs) to intelligently use external audio tools. Instead of costly internal training, it learns a lightweight routing policy to decide when to call specialized tools. The system achieved substantial benchmark improvements while requiring up to 600 times less training data to master tool usage compared to conventional methods, offering a scalable path for advanced audio AI.