Yikai Wang

A Ph.D. Student at UNC

ykwang@unc.edu

Welcome to My Homepage!

Yikai selfie

I'm Yikai Wang, currently pursuing a Ph.D. degree in the Statistics and Operations Research Department (STOR) at the University of North Carolina at Chapel Hill (UNC).

I received my B.S. degree in Computer Science from Zhejiang University, China.

My research interests include large language models, reinforcement learning and optimization.

Outside research, I enjoy playing Weiqi, astronomy, history, movies, music, all kinds of sports, and the many curious facts and stories that make the world endlessly interesting. I also play the bamboo flute and guitar.

Blogs

tmux Is Not a Shortcut Cheat Sheet. It Is a Terminal Workflow.

March 19, 2026
tmux becomes a tool you actually want to use every day only when sessions, windows, panes, reconnects, configuration, and common mistakes all fit into one mental model.

March 18 Group Meeting Notes: Agentic Vending, Pre-Training Data, and Constitutional Classifiers

March 19, 2026
Cleaned-up listener notes from a March 18 group meeting hosted by my professor, covering Project Vend 2, data poisoning, token-level filtering, duplication, replayed pre-training data, and Anthropic's next-generation constitutional classifiers.

verl, vLLM, and FlashAttention: How the Stack Actually Fits Together

March 17, 2026
A practical guide to what verl, vLLM, and FlashAttention each do, why they appear in the same post-training setup, and where their responsibilities actually differ.

GRPO and Its Variants: What Actually Changes, and Why It Matters

March 17, 2026
A long-form engineering guide to GRPO, Dr.GRPO, DAPO, BNPO, REINFORCE++, RLOO, and newer trainer variants, with an emphasis on normalization, length bias, and practical training choices for LLM RL.

PPO Is Not Just a Clip Trick

March 17, 2026
Why the practical success of PPO comes from the whole implementation stack rather than the clipping term alone.

Teaching

  • STOR 113: Decision Models for Business and Economics

    Terms: 2023 Fall | Role: Teaching Assistant

    An introduction to multivariable quantitative models in economics. Mathematical techniques for formulating and solving optimization and equilibrium problems are developed, including elementary models under uncertainty.

  • STOR 455: Methods of Data Analysis

    Terms: 2024 Spring, 2024 Fall | Role: Teaching Assistant

    Review of basic inference; two-sample comparisons; correlation; introduction to matrices; simple and multiple regression (including significance tests, diagnostics, and variable selection); analysis of variance; use of statistical software. Honors version available.

  • STOR 155: Introduction to Data Models and Inference

    Terms: 2024 Summer, 2025 Summer | Role: Instructor

    Data analysis; correlation and regression; sampling and experimental design; basic probability (random variables, expected values, normal and binomial distributions); hypothesis testing and confidence intervals for means, proportions, and regression parameters; use of spreadsheet software. Honors version available.

  • STOR 120: Foundations of Statistics and Data Science

    Terms: 2025 Spring, 2025 Fall | Role: Lab Instructor

    The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design. Honors version available.