@inproceedings{huang-etal-2026-masked,
    title = "Masked Text-to-Audio Flow-Matching and Reward Feedback Optimization",
    author = "Huang, Rongjie  and
      Yang, Dongchao  and
      Guo, Wenxiang  and
      Liu, Huadai  and
      Cheng, Xize  and
      Wang, Zehan  and
      Zhao, Zhou  and
      Wu, Xixin  and
      Meng, Helen M.",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1891/",
    pages = "37953--37966",
    ISBN = "979-8-89176-395-1",
    abstract = "Flow-matching generative models have created significant milestones in text-to-audio generation, powered by scalable training with increased data, computational resources, and model size, while their scalable inference remains less explored. In this work, we propose MaskAudioFlow, a continuous flow-matching transformer with masked generative modeling designed for scaling text-to-audio inference-time prediction. Specifically, MaskAudioFlow 1) masks spans of audio frames in training and approximates the continuous velocity vector field with flow-matching objective, and 2) performs inference via masked prediction, where we mask out generation and re-predict them through iterative decoding. To reduce the gap between generation and human preferences, we fine-tune MaskAudioFlow using reward signals from text-audio correspondence and perceptual aesthetics. Experimental results demonstrate that MaskAudioFlow achieves state-of-the-art performance in text-to-audio generation, effectively scaling inference-time computation through iterative masked prediction. Moreover, the preference-tuned model demonstrates superior text-audio alignment faithfulness and enhanced perceptual aesthetics. Audio samples are available at https://MaskAudio.github.io"
}