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cs.LG2026
Understanding and Accelerating the Training of Masked Diffusion Language Models
Chunsan Hong, Sanghyun Lee, Chieh-Hsin Lai +5
Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models (ARMs) for language modeling. However, MDMs are known to learn substantially more sl…
cs.LG2025
Lookahead Unmasking Elicits Accurate Decoding in Diffusion Language Models
Sanghyun Lee, Seungryong Kim, Jongho Park +1
Masked Diffusion Models (MDMs) as language models generate by iteratively unmasking tokens, yet their performance crucially depends on the inference time order of unmasking. Prevai…
cs.LG2025
Effective Test-Time Scaling of Discrete Diffusion through Iterative Refinement
Sanghyun Lee, Sunwoo Kim, Seungryong Kim +2
Test-time scaling through reward-guided generation remains largely unexplored for discrete diffusion models despite its potential as a promising alternative. In this work, we intro…