Junbo Li

I’m a first-year Ph.D. student in Computer Science at UT Austin, fortunately co-advised by Prof. Atlas Wang and Prof. Qiang Liu. My current research focuses on LLM post-training (alignment, reasoning and reinforcement learning), optimization and probabilistic machine learning. Below is a selected list of research areas I’ve worked on (red denotes first-author contributions):

1. LLM post-training:
   • Alignment, reasoning and reinforcement learning: Prior-Informed Preference Alignment (ICML 2025)
   • Multimodality, agent, test-time scaling: Web2Code (NeurIPS 2024)
2. LLM pre-training:
   • General / efficient / distributed optimization: Hierarchical Asynchronous Local SGD (ICML 2025), FedNAR (NeurIPS 2023)
   • Scaling law, data mixture, hyper-parameter transfer, code LLMs: LLM360 (COLM 2024), CrystalCoder (COLM2024)

3. Probabilistic machine learning:
   Sparse Subspace Variational Inference (ICLR 2024), Score Matching for Reinforcement Learning (NeurIPS 2022)

4. Trustworthy machine learning:
   ViP (ECCV 2022)

I was a visiting student with Prof. Eric Xing from 2023 to 2024 and also with Prof. Ruqi Zhang in 2023. I earned my master’s degree in computer science from UC Santa Cruz in 2023 and my bachelor’s degree in mathematics and applied mathematics from Fudan University in 2021. During my undergraduate, I worked with Prof. Zhaoran Wang and Zhuoran Yang. In high school, I studied math competitions for one year, and won a silver medal in the China Mathematical Olympiad (CMO).