I am a Research Scientist at OpenAI.

At OpenAI, my research focuses on reasoning and agents. I was a Foundational Contributor of o1-preview (Sep. 2024), o1 (Dec. 2024), Deep Research (Feb. 2025). I currently focus on coding agents and led the Codex mini model-training.

Prior to OpenAI, I was at Google Brain where I worked on whatever bottleneck stood in the way of further scaling: building JAX-based large-scale training system (T5X), training large models (e.g. PaLM), instruction fine-tuning (Flan-PaLM and Flan-T5 model families) and reasoning.

Prior to Google Brain, I did my PhD at MIT.

I am originally from South Korea (Korean name: 정형원) and currently live in Mountain View, CA.

Selected work at OpenAI

o1 launch livestream

Researchers behind o1

o1-preview demo 1

o1-preview demo 2

o1-preview demo 3

Codex mini demo

OpenAI Dev Day 2024

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Selected Lectures and Talks

Stanford CS 25 lecture

MIT EI seminar: “Don’t teach. Incentivize”

Seoul National University seminar

NYU CSCI 2590 lecture

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Recent papers (latest first)

• BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents
  Jason Wei, Zhiqing Sun, Spencer Papay, Scott McKinney, Jeffrey Han, Isa Fulford, Hyung Won Chung, Alex Tachard Passos, William Fedus, Amelia Glaese.
  
• Measuring Short-Form Factuality in Large Language Models
  Jason Wei, Karina Nguyen, Hyung Won Chung, Yunxin Joy Jiao, Spencer Papay, Amelia Glaese, John Schulman, William Fedus.
  
• Deliberative Alignment: Reasoning Enables Safer Language Models
  Melody Y. Guan, Manas Joglekar, Eric Wallace, Saachi Jain, Boaz Barak, Alec Heylar, Rachel Dias, Andrea Vallone, Hongyu Ren, Jason Wei, and others.
  
• OpenAI o1 System Card
  Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, and others.
  
• GPT-4o System Card
  Aaron Hurst, Adam Lerer, Adam P. Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, and others.
  
• GPT-4 Technical Report
  Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and others.
  
• Flan-MoE: Scaling Instruction-Finetuned Language Models with Sparse Mixture of Experts
  Sheng Shen, Le Hou, Yanqi Zhou, Nan Du, Shayne Longpre, Jason Wei, Hyung Won Chung, Barret Zoph, William Fedus, Xinyun Chen, and others.
  
• Scaling Instruction-Finetuned Language Models
  Hyung Won Chung*, Le Hou*, Shayne Longpre*, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, Jason Wei*.
  Journal of Machine Learning Research (2024)
• PaLM: Scaling Language Modeling with Pathways
  Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung and 67 others.
  Journal of Machine Learning Research (2024)
• Large Language Models Encode Clinical Knowledge
  Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, ….
  Nature 620 , 172–180 (2023)
• Scaling Up Models and Data with t5x and seqio
  Adam Roberts*, Hyung Won Chung*, Anselm Levskaya*, Gaurav Mishra*, James Bradbury\*, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Jonathan H. Clark, Stephan Lee, Dan Garrette, James Lee-Thorp, Colin Raffel, Noam Shazeer, Marvin Ritter, Maarten Bosma, Alexandre Passos, Jeremy Maitin-Shepard, Noah Fiedel, Mark Omernick, Brennan Saeta, Ryan Sepassi, Alexander Spiridonov, Joshua Newlan, Andrea Gesmundo.
  Journal of Machine Learning Research (2024)
• UniMax: Fairer and more Effective Language Sampling for Large-Scale Multilingual Pretraining
  Hyung Won Chung^*, Noah Constant^*, Xavier Garcia^*, Adam Roberts, Yi Tay, Sharan Narang, Orhan Firat.
  ICLR (2023)
• The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
  Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V. Le, Barret Zoph, Jason Wei, Adam Roberts.
  ICML (2023)
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
  BigScience Workshop (300+ authors).
  arXiv (2022)
• Transcending Scaling Laws with 0.1 % Extra Compute
  Yi Tay, Jason Wei, Hyung Won Chung, and 20 others.
  arXiv (2022)
• Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
  Mirac Suzgun, Nathan Scales, Nathanael Scharli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, Jason Wei.
  arXiv (2022)
• Language Models are Multilingual Chain-of-Thought Reasoners
  Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, Jason Wei.
  arXiv (2022)
• Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?
  Yi Tay, Mostafa Dehghani, Samira Abnar, Hyung Won Chung, William Fedus, Jinfeng Rao, Sharan Narang, Vinh Q. Tran, Dani Yogatama, Donald Metzler.
  arXiv (2022)
• UL2: Unifying Language Learning Paradigms
  Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Denny Zhou, Neil Houlsby, Donald Metzler.
  arXiv (2022)
• What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?
  Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy, Julien Launay, Colin Raffel.
  ICML (2022)
• PaLM: Scaling Language Modeling with Pathways
  Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung and 67 others.
  arXiv (2022)
• Scaling Up Models and Data with t5x and seqio
  Adam Roberts^*, Hyung Won Chung^*, Anselm Levskaya^*, Gaurav Mishra^*, James Bradbury^*.
  arXiv (2022)
• Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers
  Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler.
  ICLR (2022)
• Charformer: Fast Character Transformers via Gradient-based Subword Tokenization
  Yi Tay, Vinh Q. Tran, Sebastian Ruder, Jai Gupta, Hyung Won Chung, Dara Bahri, Zhen Qin, Simon Baumgartner, Cong Yu, Donald Metzler.
  ICLR (2022)
• Do Transformer Modifications Transfer Across Implementations and Applications?
  Sharan Narang, Hyung Won Chung, Yi Tay, William Fedus, Thibault Fevry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, Yanqi Zhou, Wei Li, Nan Ding, Jake Marcus, Adam Roberts, Colin Raffel.
  EMNLP (2021)
• Neural Data Augmentation via Example Extrapolation
  Kenton Lee^*, Kelvin Guu^*, Luheng He^*, Tim Dozat^*, Hyung Won Chung^*.
  arXiv (2021)
• Rethinking Embedding Coupling in Pre-trained Language Models
  Hyung Won Chung^*, Thibault Févry^*, Henry Tsai, Melvin Johnson, Sebastian Ruder.
  ICLR (2021)
• Improving Multilingual Models with Language-Clustered Vocabularies
  Hyung Won Chung^*, Dan Garrette, Kiat Chuan Tan, Jason Riesa.
  EMNLP (2020)