Shuibai Zhang (张水柏) | University of Wisconsin-Madison

About Me

I am a second-year Computer Science Ph.D. student at the University of Wisconsin-Madison. I received dual bachelor's degrees from a joint program of the University of Electronic Science and Technology of China (UESTC) and the University of Glasgow.

My research focuses on the post-training stages of large foundation models, with the aim of making them more efficient and intelligent. Recently, I have become interested in diffusion language models, which offer inherent advantages such as parallel decoding and built-in error correction, and may hold the potential to reshape future paradigms of language modeling.

As an impact-driven researcher, I aspire for my work to drive progress not only in methodology but also in how large foundation models positively transform science, technology, and society.

Projects

🔥 Open-dCoder: The First Fully Open Diffusion LLM for Code

A 0.5B diffusion coding model with pretraining pipeline, inference code, checkpoints, and evaluation suite.

Open Source Project

Preprints

Planner Aware Path Learning in Diffusion Language Models Training

Fred Zhangzhi Peng, Zachary Bezemek, Jarrid Rector-Brooks, Shuibai Zhang, Anru R. Zhang, Michael Bronstein, Avishek Joey Bose, Alexander Tong

ArXiv

ParallelBench: Understanding the Tradeoffs of Parallel Decoding in Diffusion LLMs

Wonjun Kang, Kevin Galim, Seunghyuk Oh, Minjae Lee, Yuchen Zeng, Shuibai Zhang, Coleman Hooper, Yuezhou Hu, Hyung Il Koo, Nam Ik Cho, Kangwook Lee

ArXiv

ReJump: A Tree-Jump Representation for Analyzing and Improving LLM Reasoning

Yuchen Zeng*, Shuibai Zhang*, Wonjun Kang*, et al., Dimitris Papailiopoulos, Kangwook Lee

ArXiv equal contribution