Dream-7B-Instruct
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- Controlled study examines learned unmasking for diffusion language models
A preprint submitted to arXiv on October 22, 2026 presents a controlled study of learned unmasking policies for masked diffusion language models, separating the effects of teacher labels, imitation states, policy inputs, capacity, and compute overhead. Its 856K-parameter trajectory-contraction policy achieved the reported best accuracy–NFE frontier and highest GSM8K throughput on LLaDA-8B-Instruct, with competitive results on MATH-500 and HumanEval and transfer largely to Dream-7B-Instruct.
Hugging Face · Papers · 🔥 0
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