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    PUMBA paper explores a training-sampling gap in masked diffusion language models

    An author says the paper examines how training could better reflect the paths models follow when generating text.

    Kyle KastnerKK
    Manuel MadeiraMM
    2 Sources, 5h ago, first seen 5h ago

    TLDR

    An author introducing PUMBA says masked diffusion language models are trained one way and sampled another. The paper studies how training could better account for the paths models follow during inference, and how that might lead to more efficient text generation.

    Combined views

    1.3K

    2 Sources, first seen 5h ago

    Combined views

    1.3K

    2 Sources, first seen 5h ago

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    31 reposts

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    2 Sources

    Manuel Madeira@manuelmlmadeiraSay hi to PUMBA! 🐗 Masked diffusion language models are trained one way and sampled another. Our new paper studies how to make MDM training more aware of the trajectories followed at inference, and how this can lead to more efficient text generation. Paper link below 🧵5h
    Kyle Kastner@kastnerkyleRT @manuelmlmadeira: Say hi to PUMBA! 🐗 Masked diffusion language models are trained one way and sampled another. Our new paper studies h…1h

    2 Sources

    Manuel Madeira@manuelmlmadeiraSay hi to PUMBA! 🐗 Masked diffusion language models are trained one way and sampled another. Our new paper studies how to make MDM training more aware of the trajectories followed at inference, and how this can lead to more efficient text generation. Paper link below 🧵5h
    Kyle Kastner@kastnerkyleRT @manuelmlmadeira: Say hi to PUMBA! 🐗 Masked diffusion language models are trained one way and sampled another. Our new paper studies h…1h
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