1 citations · 1 across the 4 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.LG2026
Revisiting the 16x Pretraining Compute Gap between Diffusion and Autoregressive Language Models
Guanghan Wang, Bing He, Zhan Shi +3
Diffusion language models (DLMs) have attracted wide attention for their fast, parallel decoding, but they are believed to be far less efficient to train than autoregressive langua…
cs.LG2026
Clock Diffusion: Efficient Semi-Autoregressive Continuous Diffusion Language Models
Yair Schiff, Omer Belhasin, Roy Uziel +7
Recent works on continuous diffusion for discrete data have demonstrated performance on par with comparable discrete diffusion models. However, these continuous counterparts lack k…
cs.LG2026
Learn from Your Mistakes: Self-Correcting Masked Diffusion Models
Yair Schiff, Omer Belhasin, Roy Uziel +6
Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models, enabling parallel token generation while achieving competitive performance. Despite…