6 papers
How Should Diffusion Language Models Edit Code?
Xijia Tao, Ziru Liu, Shansan Gong +7
Code editing requires a model to decide where to make changes, generate the new content, and preserve everything else. We study how masked diffusion language models divide these re…
DreamReasoner-8B: Block-Size Curriculum Learning for Diffusion Reasoning Models
Zirui Wu, Lin Zheng, Jiacheng Ye +5
Block diffusion language models accelerate decoding through parallel block-wise denoising, yet whether they can be reliably scaled for long chain-of-thought (CoT) reasoning remains…
DreamOn: Diffusion Language Models For Code Infilling Beyond Fixed-size Canvas
Zirui Wu, Lin Zheng, Zhihui Xie +8
Diffusion Language Models (DLMs) present a compelling alternative to autoregressive models, offering flexible, any-order infilling without specialized prompting design. However, th…
Accelerating Diffusion LLM Inference via Local Determinism Propagation
Fanheng Kong, Jingyuan Zhang, Yahui Liu +4
Diffusion large language models (dLLMs) represent a significant advancement in text generation, offering parallel token decoding capabilities. However, existing open-source impleme…
Dream-Coder 7B: An Open Diffusion Language Model for Code
Zhihui Xie, Jiacheng Ye, Lin Zheng +8
We present Dream-Coder 7B, an open-source discrete diffusion language model for code generation that exhibits emergent any-order generation capabilities. Unlike traditional autoreg…
Dream 7B: Diffusion Large Language Models
Jiacheng Ye, Zhihui Xie, Lin Zheng +5
We introduce Dream 7B, the most powerful open diffusion large language model to date. Unlike autoregressive (AR) models that generate tokens sequentially, Dream 7B employs discrete…