arXiv Computation and Language
Self-Regularized Flow Language Models
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
arXiv AI
Jun 8
Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation
arXiv:2606. 06712v1 Announce Type: cross Abstract: We study the transformation of autoregressive models (ARLMs) into diffusion language models (DLMs).
Hugging Face Trending Papers
Oct 6
Enhancing Diffusion Language Models with Autoregressive Post-Training Weights
Diffusion language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) language models, offering flexible token-update orders and parallel decoding. Recent dLLMs are often in...
arXiv Computation and Language
Aug 25
SelFusion: Self-distillation for Diffusion Language Models
arXiv:2608.22898v1 Announce Type: new Abstract: Diffusion language models (DLMs) alleviate the inherent latency bottleneck of autoregressive (AR) large language models (LLMs), but their degraded gene...
arXiv AI
Sep 30
On Trajectory-Aware Training for Masked Diffusion Language Models
arXiv:2609.37974v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) generate text by unmasking several tokens per step, but they are trained and sampled under different conditions. The m...
arXiv Machine Learning
Oct 7
Enhancing Diffusion Language Models with Autoregressive Post-Training Weights
arXiv:2610.08108v1 Announce Type: new Abstract: Diffusion language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) language models, offering flexible token-update orders...
arXiv Machine Learning
Jul 7
Few-Step Diffusion Language Models via Trajectory Self-Distillation
arXiv:2602. 12262v4 Announce Type: replace-cross Abstract: Diffusion large language models (DLLMs) have emerged as powerful generative models with the promise of fast text generation through parallel decoding.