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HC-DLM couples token readouts to a persistent continuous diffusion state

huggingface.co by Hui Ren et al. Editor score 7/10

HC-DLM reads tokens from a shared latent at each denoising step and feeds them back as a scaffold, with training derived from a variational bound on token likelihood. The design targets the dependency loss of parallel discrete diffusion, though the excerpt gives no results.

What

HC-DLM couples discrete token generation with a continuous latent trajectory in one denoising process.

Method

Tokens are read from the latent at each step and feed back as a scaffold for its next update; the latent is the only persistent generative state.

Why it matters

At matched model size, it improves over discrete and continuous diffusion baselines on Sudoku, Countdown, and LM1B.

Read on huggingface.co → Code · 1 stars

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