AVRT: Audio-Visual Reasoning Transfer through Single-Modality Teachers
Abstract
Recent advances in reasoning models have shown remarkable progress in text-based domains, but transferring those capabilities to multimodal settings, e.g., to allow reasoning over audio-visual data, still remains a challenge, in part because of the limited availability of high-quality reasoning data in targeted multimodal combinations. To address this problem, we introduce AVRT, a novel framework that generates high-quality audio-visual reasoning traces from single-modality teacher models. We generate independent vision- and audio-reasoning traces via models specialized to reason over their respective modalities and merge the resulting traces with an LLM merger model. The resulting multimodal traces are used in a supervised fine-tuning (SFT) cold start to adapt the target model to audio-visual reasoning traces first, before training it in a second reinforcement learning stage on larger-scale data. Evaluated on seven audio-visual and audio benchmarks, our 3B and 7B parameter models achieve state-of-the-art results among models of comparable size including OmniBench and DailyOmni for audio-visual and MMAR for audio-only reasoning, showing that cross-modal training also transfers to single-modality tasks and establishing a new training pipeline for multimodal reasoning models.
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 paper presents AVRT, a framework that effectively generates high-quality audio-visual reasoning traces by merging outputs from single-modality teacher models, showcasing significant advancements in multimodal reasoning capabilities. The comprehensive methodology, rigorous experimental evaluation, and potential for broader applications underscore its importance in the field of machine learning.
Methodology
The AVRT framework introduces a novel approach for generating audio-visual reasoning traces by leveraging single-modality teacher models. The methodology is well-structured, comprising two main stages: reasoning extraction from specialized models and cross-modal aggregation using a text-only LLM. This approach allows for high-quality reasoning trace generation without requiring extensive multimodal training data, addressing a significant gap in the current literature. The use of a two-stage training pipeline (supervised fine-tuning followed by reinforcement learning) is a thoughtful design choice that enhances the model's reasoning capabilities.
Results & evaluation
The experiments are comprehensive, evaluating the proposed model on seven benchmark datasets, which include both audio-visual and audio-only reasoning tasks. The results demonstrate that the AVRT model achieves state-of-the-art performance among comparable models, indicating the effectiveness of the proposed methodology. The ablation studies provide insight into the contributions of different components of the training process, reinforcing the validity of the findings.
Reproducibility
The paper mentions that all code, data, and checkpoints will be made available, which is a positive aspect for reproducibility. However, specific implementation details, such as hyperparameters and training configurations, are provided, which aids in replicating the experiments. The clarity of the methodology and the structured presentation of results further enhance reproducibility.
Limitations
One limitation noted is the reliance on the quality of the single-modality teacher models, which could affect the overall performance of the AVRT framework. Additionally, while the model shows strong performance, the potential for hallucinations in reasoning traces, as indicated in the qualitative analysis, suggests that further refinement is needed to improve the accuracy of generated outputs.
Broader impact
The implications of this research are significant, as it opens avenues for improved audio-visual reasoning capabilities in AI systems. The ability to generate high-quality reasoning traces from single-modality teachers could facilitate advancements in various applications, including multimedia understanding, interactive AI systems, and enhanced human-computer interaction. The paper presents AVRT, a framework that effectively generates high-quality audio-visual reasoning traces by merging outputs from single-modality teacher models, showcasing significant advancements in multimodal reasoning capabilities. The comprehensive methodology, rigorous experimental evaluation, and potential for broader applications underscore its importance in the field of machine learning.
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