Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling
  Paradigms for Text-to-Music Generation
Recent progress in text-to-music generation has enabled models to synthesize
high-quality musical segments, full compositions, and even respond to
fine-grained control signals, e.g. chord progressions. State-of-the-art (SOTA)
systems differ significantly across many dimensions, such as training datasets,
modeling paradigms, and architectural choices. This diversity complicates
efforts to evaluate models fairly and pinpoint which design choices most
influence performance. While factors like data and architecture are important,
in this study we focus exclusively on the modeling paradigm. We conduct a
systematic empirical analysis to isolate its effects, offering insights into
associated trade-offs and emergent behaviors that can guide future
text-to-music generation systems. Specifically, we compare the two arguably
most common modeling paradigms: Auto-Regressive decoding and Conditional
Flow-Matching. We conduct a controlled comparison by training all models from
scratch using identical datasets, training configurations, and similar backbone
architectures. Performance is evaluated across multiple axes, including
generation quality, robustness to inference configurations, scalability,
adherence to both textual and temporally aligned conditioning, and editing
capabilities in the form of audio inpainting. This comparative study sheds
light on distinct strengths and limitations of each paradigm, providing
actionable insights that can inform future architectural and training decisions
in the evolving landscape of text-to-music generation. Audio sampled examples
are available at: https://huggingface.co/spaces/ortal1602/ARvsFM
684982b5d546b83e67deb341
Or Tal
claimed_verified
2025-06-12T12:42:30.762Z
64de1ffa016e232d2eed1f23
/avatars/6e6aa0984302254d28a659529f874740.svg
Or Tal
user
ortal1602
684982b5d546b83e67deb342
Felix Kreuk
extracted_pending
2025-06-11T13:20:53.749Z
66f526d04970b1adc2651d7e
/avatars/2dd952cba07867a9e9ab443b37052a95.svg
Felix Kreuk
user
felixkreuk1
684982b5d546b83e67deb343
Yossi Adi
Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling
  Paradigms for Text-to-Music Generation
Recent progress in text-to-music generation has enabled models to synthesize
high-quality musical segments, full compositions, and even respond to
fine-grained control signals, e.g. chord progressions. State-of-the-art (SOTA)
systems differ significantly across many dimensions, such as training datasets,
modeling paradigms, and architectural choices. This diversity complicates
efforts to evaluate models fairly and pinpoint which design choices most
influence performance. While factors like data and architecture are important,
in this study we focus exclusively on the modeling paradigm. We conduct a
systematic empirical analysis to isolate its effects, offering insights into
associated trade-offs and emergent behaviors that can guide future
text-to-music generation systems. Specifically, we compare the two arguably
most common modeling paradigms: Auto-Regressive decoding and Conditional
Flow-Matching. We conduct a controlled comparison by training all models from
scratch using identical datasets, training configurations, and similar backbone
architectures. Performance is evaluated across multiple axes, including
generation quality, robustness to inference configurations, scalability,
adherence to both textual and temporally aligned conditioning, and editing
capabilities in the form of audio inpainting. This comparative study sheds
light on distinct strengths and limitations of each paradigm, providing
actionable insights that can inform future architectural and training decisions
in the evolving landscape of text-to-music generation. Audio sampled examples
are available at: https://huggingface.co/spaces/ortal1602/ARvsFM
A systematic comparison of Auto-Regressive decoding and Conditional Flow-Matching in text-to-music generation highlights distinct strengths and limitations of each modeling paradigm.
Auto-Regressive decoding Conditional Flow-Matching generation quality robustness scalability textual conditioning temporal alignment audio inpainting