arXiv:2603.01006v2 Announce Type: replace-cross Abstract: REPresentation Alignment (REPA) improves the training of generative flow models by aligning intermediate hidden states with pretrained teacher features, but its effectiveness in token-conditioned audio Flow Matching critically depends on the choice of supervised layers, which is typically made heuristically based on the depth. In this work, we introduce Attribution-Guided REPresentation Alignment (AG-REPA), a novel causal layer selection strategy for representation alignment in audio Flow Matching. Firstly, we find that layers that best
The increasing complexity and performance demands of generative AI models, particularly in audio, necessitate more efficient and robust training methodologies.
This development offers a refined method for improving the training stability and effectiveness of advanced audio generative AI, leading to more realistic and controllable synthetic audio.
The heuristic approach to layer selection in representation alignment for audio Flow Matching is replaced by a more principled, attribution-guided causal selection strategy.
- · AI researchers and developers
- · Creative industries using generative audio
- · Companies building audio-centric AI applications
- · Developers reliant on less efficient, heuristic training methods
Improved performance and reduced training time for audio generative models using flow matching techniques.
Faster development and deployment of sophisticated AI agents capable of higher-fidelity audio synthesis and understanding.
Enhanced human-computer interaction through more natural and realistic audio interfaces and generative audio content creation.
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