About Me
I am actively looking for PhD opportunities for 2027.
I am currently a Research Assistant at the AGI Lab, Westlake University, advised by Prof. Chi Zhang. Concurrently, I serve as a Remote Research Intern at the NLP Lab, University of California, Merced, under the supervision of Prof. Yiwei Wang. Prior to this, I received my B.Eng. degree in Artificial Intelligence from Hebei University of Technology.
My research interests focus on Multimodal dLLMs/LLMs, Reinforcement Learning, and dLLM/LLM Agents.
Publications
-
Learning When to Parallelize: Structure-Aware RL for Logical Reasoning in Diffusion LLMsSubmitted
Education
Hebei University of Technology
B.Eng. in Artificial Intelligence
Sep 2020 – Jun 2024
Experience
NLP Lab, University of California, Merced
Remote Research Intern
Advisor: Prof. Yiwei Wang
May 2025 – Present
Research Projects
Optimizing Diffusion LLMs via Logic-Aware Reinforcement Learning Reward Design
Lead Project Member · Nov 2025 – Jan 2026
Advisors: Prof. Chi Zhang, Prof. Yiwei Wang
- Proposed and implemented a logic-aware RL reward design to address autoregressive degradation in complex reasoning tasks.
- Constructed a multi-dimensional reward mechanism — encompassing parallelism, dependency, and result correctness rewards — to guide the optimization of diffusion LLMs through logical structure constraints and generation parallelism.
- Systematically evaluated on the ListOps reasoning benchmark, exploring robustness across various positional sampling algorithms. Significantly enhanced generation quality for complex logical tasks while substantially improving parallel decoding efficiency.
Training-Free Format Control for Large Diffusion Language Models
Independent Researcher · Jun 2025 – Oct 2025
Advisors: Prof. Chi Zhang, Prof. Yiwei Wang
- Implemented a training-free inference algorithm for large diffusion language models, utilizing token-level softmax scores to guide dynamic anchor selection, ensuring robust structural integrity across diverse scenarios in zero-shot settings.
- Conducted rigorous evaluations on mathematical reasoning (GSM8K, MATH) and JSON benchmarks against Dream-7B and static infilling baselines.
- Significantly enhanced format adherence while preserving reasoning accuracy: achieved over 70% format retention in mathematical reasoning and maintained a stable ~80% valid JSON generation rate across various extraction methods.
Skills
Programming: Python, C, Shell/Bash, Markdown
Frameworks & Ecosystem: PyTorch, HuggingFace, WandB, Tree-sitter
Soft Skills: Synergistic Collaborator, Intrinsically Motivated, Intellectually Curious
AI-Assisted Development: 100M+ tokens/month vibe coding