1 citations · 1 across the 7 of their papers we have counts for
9 papers
LLaDA2.0: Scaling Up Diffusion Language Models to 100B
Tiwei Bie, Maosong Cao, Kun Chen +28
This paper presents LLaDA2.0 -- a tuple of discrete diffusion large language models (dLLM) scaling up to 100B total parameters through systematic conversion from auto-regressive (A…
Knocking-Heads Attention
Zhanchao Zhou, Xiaodong Chen, Haoxing Chen +2
Multi-head attention (MHA) has become the cornerstone of modern large language models, enhancing representational capacity through parallel attention heads. However, increasing the…
dInfer: An Efficient Inference Framework for Diffusion Language Models
Yuxin Ma, Lun Du, Lanning Wei +20
Diffusion-based large language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) LLMs, leveraging denoising-based generation to enable inherent parallel…
DND: Boosting Large Language Models with Dynamic Nested Depth
Tieyuan Chen, Xiaodong Chen, Haoxing Chen +3
We introduce Dynamic Nested Depth (DND), a novel method that improves performance for off-the-shelf LLMs by selecting critical tokens to reprocess in a nested depth manner. Specifi…
LLaDA-MoE: A Sparse MoE Diffusion Language Model
Fengqi Zhu, Zebin You, Yipeng Xing +23
We introduce LLaDA-MoE, a large language diffusion model with the Mixture-of-Experts (MoE) architecture, trained from scratch on approximately 20T tokens. LLaDA-MoE achieves compet…
MultiEdit: Advancing Instruction-based Image Editing on Diverse and Challenging Tasks
Mingsong Li, Lin Liu, Hongjun Wang +7
Current instruction-based image editing (IBIE) methods struggle with challenging editing tasks, as both editing types and sample counts of existing datasets are limited. Moreover,…