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Diffusion language models can generate text in parallel, but their quality lags. Adaptive multi-step lookahead decoding improves this by refining masked tokens more efficiently. This could make diffusion models more viable for real-world text generation tasks.
“arXiv:2607.15655v1 Announce Type: cross Abstract: Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternative to autoregressive decoding. Recent lookahead-b…”
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ENTITY
Diffusion Language Models, Adaptive Multi-Step Lookahead Decoding
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LAST OBSERVED
Aug 6, 2026