Hao (Jack) BAI

Hao (Jack) BAI

CS M.S. Candidate

University of Illinois, Urbana Champaign

Hi, there! I’m Jack. I’m an M.S. student in Computer Science at UIUC, where I’m fully devoted to autonomous intelligence. I work on this problem upon two levels:

  • I study representation learning for foundation models in order to interpret their behaviors.
  • I study reinforcement learning algorithms for foudation models based on the so-derived understandings.

I’m also slightly involved in data augmentation to enhance intelligence.

Bio: Jack Bai is an M.S. student in Computer Science at UIUC, advised by Prof. Nan Jiang and Heng Ji, and a visiting scholar at BAIR, advised by Prof. Sergey Levine and Yi Ma. Before master’s, he obtained a dual degree from Zhejiang University and UIUC in Computer Engineering. During those wonderful years, he interned at Microsoft Research advised by Dr. Shilin He, and participated in the Amazon SocialBot Challenge advised by Prof. Chengxiang Zhai.

  • Representation Learning
  • Reinforcement Learning
  • Data-enhanced Fine-tuning
  • MS in Computer Science, 2025

    University of Illinois, Urbana Champaign

  • BS in Computer Engineering, 2023

    University of Illinois, Urbana-Champaign


We have to learn the bitter lesson that building in how we think we think does not work in the long run. The two methods that seem to scale arbitrarily … are learning and search. Learning is to use data to extract patterns, and search is the optimization procedure that uses computation to make rational decisions.

Richard Sutton, The Bitter Lesson, 2019

Data without optimization doesn’t allow to solve new problems in new ways; optimization without data is hard to apply to the real world outside of simulators.

Sergey Levine, RL with Large Datasets, 2023


  • Only referred collaboration from UC Berkeley will be considered (updated SP24)

Core Publications

Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning (Preprint)
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is? (JMLR)
CharmBana: Progressive Responses with Real-Time Internet Search for Knowledge-Powered Conversations (WSDM-24)
Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations (EMNLP-23)