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Jun Zhou

5 papers hereh-index 51.1k citations15 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CL2
  • cs.LG2
  • cs.CV1
same name
  • Jun Zhou — 28 papers, h 26
  • Jun Zhou — 17 papers, h 20
  • Jun Zhou — 16 papers
  • Jun Zhou — 16 papers, h 22
  • Jun Zhou — 15 papers
  • Jun Zhou — 13 papers, h 7

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators

5 papers

cs.CL2025

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…

cs.LG2025

LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning

Zebin You, Shen Nie, Xiaolu Zhang +5

In this work, we introduce LLaDA-V, a purely diffusion-based Multimodal Large Language Model (MLLM) that integrates visual instruction tuning with masked diffusion models, represen…

cs.LG2025

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…

cs.CV2025

Effective and Efficient Masked Image Generation Models

Zebin You, Jingyang Ou, Xiaolu Zhang +3

Although masked image generation models and masked diffusion models are designed with different motivations and objectives, we observe that they can be unified within a single fram…

cs.CL2025

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…

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