AudioRouter: Data Efficient Audio Understanding via RL based Dual Reasoning
Abstract
Large Audio Language Models (LALMs) have demonstrated strong capabilities in audio understanding and reasoning. However, their performance on fine grained auditory perception remains unreliable, and existing approaches largely rely on data intensive training to internalize perceptual abilities. We propose AudioRouter, a reinforcement learning framework that enables LALMs to improve audio understanding by learning when and how to use external audio tools. Rather than tightly coupling tool usage with audio reasoning, AudioRouter formulates tool use as an explicit decision making problem and optimizes a lightweight routing policy while keeping the underlying reasoning model frozen. Experimental results show that AudioRouter achieves substantial improvements on standard audio understanding benchmarks while requiring up to 600x less training data to learn tool usage compared with conventional training paradigms. These findings suggest that learning effective tool usage offers a data efficient and scalable alternative to internalizing perceptual abilities in LALMs.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of AudioRouter, a reinforcement learning framework that enhances audio understanding in large audio language models by optimizing tool usage while significantly reducing the amount of required training data. This innovative approach not only improves performance but also offers a scalable alternative to traditional data-intensive training methods, marking a significant advancement in the field of audio processing and reasoning.
Methodology
The methodology presented in the paper is innovative as it decouples tool usage from the reasoning model, allowing for a more efficient learning process. The use of reinforcement learning to optimize a routing policy for tool invocation is a significant departure from traditional end-to-end training approaches. The authors effectively formulate tool usage as a discrete decision-making problem, which is a novel perspective in the context of audio language models. The decision to keep the reasoning model frozen while training the router is a strategic choice that enhances data efficiency and reduces complexity.
Results & evaluation
The experimental evaluation is robust, demonstrating the effectiveness of AudioRouter across multiple benchmarks (MMAU-mini and MMAR). The results indicate substantial improvements in performance while requiring significantly less training data compared to conventional methods. The paper provides clear comparisons against baseline models, showcasing the advantages of the proposed framework. However, the experiments could benefit from a broader range of datasets and tasks to further validate the generalizability of the approach.
Reproducibility
The paper includes sufficient details regarding the experimental setup, including model architectures, training data, and reinforcement learning specifics. However, the lack of URLs for code or project repositories limits the reproducibility of the results. Providing access to the trained models or implementation would enhance the ability of other researchers to replicate the findings.
Limitations
The paper acknowledges that the relative outcome reward relies on a fixed reasoning model, which may limit the Router's learning signal. Additionally, the focus on short-form, closed-set audio reasoning tasks with a limited set of audio tools may restrict the applicability of the findings. Future work should explore extending the framework to more complex reasoning tasks and diverse tool capabilities.
Broader impact
The proposed AudioRouter framework has the potential to significantly advance the field of audio understanding by providing a more data-efficient method for leveraging external tools. This approach could lead to broader applications in various domains, including audio analysis, multimedia processing, and interactive AI systems. By reducing the reliance on large annotated datasets, it may also democratize access to advanced audio processing capabilities. The main contribution of this paper is the introduction of AudioRouter, a reinforcement learning framework that enhances audio understanding in large audio language models by optimizing tool usage while significantly reducing the amount of required training data. This innovative approach not only improves performance but also offers a scalable alternative to traditional data-intensive training methods, marking a significant advancement in the field of audio processing and reasoning.
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