4 citations · 11 across the 19 of their papers we have counts for
22 papers
LLaDA MoE v2: Scaling Mixture-of-Experts Diffusion Language Models
Fengqi Zhu, Shaoxuan Xu, Jingyang Ou +11
Diffusion language models (dLLMs) offer an alternative to autoregressive (AR) language modeling, yet the scaling behavior of Mixture-of-Experts (MoE) dLLMs remains poorly understoo…
Improved Large Language Diffusion Models
Shen Nie, Qiyang Min, Shaoxuan Xu +7
Modern large language models are predominantly trained with autoregressive factorization and causal attention. We present \emph{iLLaDA}, an 8B masked diffusion language model train…
LLaDA-o: An Effective and Length-Adaptive Omni Diffusion Model
Zebin You, Xiaolu Zhang, Jun Zhou +2
We present \textbf{LLaDA-o}, an effective and length-adaptive omni diffusion model for multimodal understanding and generation. LLaDA-o is built on a Mixture of Diffusion (MoD) fra…
Spectral Condition for P under Width-Depth Scaling
Chenyu Zheng, Rongzhen Wang, Xinyu Zhang +1
Generative foundation models are increasingly scaled in both width and depth, posing significant challenges for stable feature learning and reliable hyperparameter (HP) transfer ac…
DLLM-Searcher: Adapting Diffusion Large Language Model for Search Agents
Jiahao Zhao, Shaoxuan Xu, Zhongxiang Sun +6
Recently, Diffusion Large Language Models (dLLMs) have demonstrated unique efficiency advantages, enabled by their inherently parallel decoding mechanism and flexible generation pa…
Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective
Jingyang Ou, Jiaqi Han, Minkai Xu +5
Reinforcement Learning (RL) has proven highly effective for autoregressive language models, but adapting these methods to diffusion large language models (dLLMs) presents fundament…
LLaDA-Rec: Discrete Diffusion for Parallel Semantic ID Generation in Generative Recommendation
Teng Shi, Chenglei Shen, Weijie Yu +6
Generative recommendation represents each item as a semantic ID, i.e., a sequence of discrete tokens, and generates the next item through autoregressive decoding. While effective,…
UltraLLaDA: Scaling the Context Length to 128K for Diffusion Large Language Models
Guangxin He, Shen Nie, Fengqi Zhu +6
Diffusion LLMs have attracted growing interest, with plenty of recent work emphasizing their great potential in various downstream tasks; yet the long-context behavior of diffusion…
Masked Diffusion Models as Energy Minimization
Sitong Chen, Shen Nie, Jiacheng Sun +4
We present a systematic theoretical framework that interprets masked diffusion models (MDMs) as solutions to energy minimization problems in discrete optimal transport. Specificall…
Reinforcing General Reasoning without Verifiers
Xiangxin Zhou, Zichen Liu, Anya Sims +6
The recent paradigm shift towards training large language models (LLMs) using DeepSeek-R1-Zero-style reinforcement learning (RL) on verifiable rewards has led to impressive advance…
LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models
Fengqi Zhu, Rongzhen Wang, Shen Nie +8
While Masked Diffusion Models (MDMs), such as LLaDA, present a promising paradigm for language modeling, there has been relatively little effort in aligning these models with human…
A Theory for Conditional Generative Modeling on Multiple Data Sources
Rongzhen Wang, Yan Zhang, Chenyu Zheng +2
The success of large generative models has driven a paradigm shift, leveraging massive multi-source data to enhance model capabilities. However, the interaction among these sources…
Large Language Diffusion Models
Shen Nie, Fengqi Zhu, Zebin You +7
The capabilities of large language models (LLMs) are widely regarded as relying on autoregressive models (ARMs). We challenge this notion by introducing LLaDA, a diffusion model tr…
Real-time Identity Defenses against Malicious Personalization of Diffusion Models
Hanzhong Guo, Shen Nie, Chao Du +3
Personalized generative diffusion models, capable of synthesizing highly realistic images based on a few reference portraits, may pose substantial social, ethical, and legal risks…
Scaling up Masked Diffusion Models on Text
Shen Nie, Fengqi Zhu, Chao Du +5
Masked diffusion models (MDMs) have shown promise in language modeling, yet their scalability and effectiveness in core language tasks, such as text generation and language underst…
Improving Long-Text Alignment for Text-to-Image Diffusion Models
Luping Liu, Chao Du, Tianyu Pang +3
The rapid advancement of text-to-image (T2I) diffusion models has enabled them to generate unprecedented results from given texts. However, as text inputs become longer, existing e…
Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations
Kaiwen Xue, Yuhao Zhou, Shen Nie +4
Bayesian flow networks (BFNs) iteratively refine the parameters, instead of the samples in diffusion models (DMs), of distributions at various noise levels through Bayesian inferen…
Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data
Jingyang Ou, Shen Nie, Kaiwen Xue +4
Discrete diffusion models with absorbing processes have shown promise in language modeling. The key quantities to be estimated are the ratios between the marginal probabilities of…
Graph Diffusion Policy Optimization
Yijing Liu, Chao Du, Tianyu Pang +3
Recent research has made significant progress in optimizing diffusion models for downstream objectives, which is an important pursuit in fields such as graph generation for drug de…
Gaussian Mixture Solvers for Diffusion Models
Hanzhong Guo, Cheng Lu, Fan Bao +4
Recently, diffusion models have achieved great success in generative tasks. Sampling from diffusion models is equivalent to solving the reverse diffusion stochastic differential eq…
The Blessing of Randomness: SDE Beats ODE in General Diffusion-based Image Editing
Shen Nie, Hanzhong Allan Guo, Cheng Lu +3
We present a unified probabilistic formulation for diffusion-based image editing, where a latent variable is edited in a task-specific manner and generally deviates from the corres…
On Memorization in Diffusion Models
Xiangming Gu, Chao Du, Tianyu Pang +3
Due to their capacity to generate novel and high-quality samples, diffusion models have attracted significant research interest in recent years. Notably, the typical training objec…