Ning Miao (苗宁)

Department of Data Science and Hong Kong Institute of AI for Science, City University of Hong Kong
Email: ningmiaocityu.edu.hk , miaoningpku.edu.cn

I am an assistant professor at the City University of Hong Kong. I got my Ph.D. from the Department of Statistics, Oxford, supervised by Prof. Tom Rainforth and Prof. Yee Whye Teh. Before that, I got my BS and MS degrees from Peking University and worked as a researcher at Bytedance AI lab. I am the PI of the Miaow Lab. Our research focuses on machine reasoning (LLM reasoning, AI4Math) and generative models.

News

* Oct 2025: We are looking for a research assistant professor to work on LLM structure or post-training. Fresh PhDs are welcome!
* Oct 2025: A limited number of PhD positions (1–2) remain available for Fall 2026 admission. Prospective applicants are encouraged (though not required) to consider a short-term internship at Miaow Lab prior to applying.

Publications

• Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey
  Qiyuan Liu, Hao Xu, Xuhong Chen, Wei Chen, Yee Whye Teh, Ning Miao
  In Arxiv.
  [pdf]
• MARCOS: Deep Thinking by Markov Chain of Continuous Thoughts
  Jiayu Liu, Zhenya Huang, Anya Sims, Enhong Chen, Yee Whye Teh, Ning Miao
  In Arxiv.
  [pdf]
• BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design
  Deepro Choudhury, Sinead Williamson, Adam Goliński, Ning Miao, Freddie Bickford Smith, Michael Kirchhof, Yizhe Zhang, Tom Rainforth
  In Arxiv.
  [pdf]
• SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning
  Ning Miao, Yee Whye Teh, Tom Rainforth
  In ICLR, 2024.
  [bib] [pdf] [code]
• Learning Instance-Specific Augmentations by Capturing Local Invariances
  Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim
  In ICML, 2023.
  [bib] [pdf] [code]
• On Incorporating Inductive Biases into VAEs
  Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth
  In ICLR, 2022.
  [bib] [pdf] [code]
• Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods
  Ning Miao, YuXuan Song, Hao Zhou, Lei Li
  In ACL, 2020.
  [bib] [pdf] [code]
• Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
  Wenxian Shi, Hao Zhou, Ning Miao, Shenjian Zhao, Lei Li
  In ICML, 2020.
  [bib] [pdf] [code]
• Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation
  YuXuan Song, Ning Miao, Hao Zhou, Lei Li
  In AISTATS, 2020.
  [bib] [pdf]
• Kernelized Bayesian Softmax for Text Generation
  Ning Miao, Hao Zhou, Chengqi Zhao, Wenxian Shi, Lei Li
  In NeurIPS, 2019.
  [bib] [pdf] [code]
• Generating Fluent Adversarial Examples for Natural Languages
  Huangzhao Zhang, Hao Zhou, Ning Miao, Lei Li
  In ACL, 2019.
  [bib] [pdf] [code]
• Constrained Sentence Generation via Metropolis-Hastings Sampling
  Ning Miao, Hao Zhou, Lili Mou, Rui Yan, Lei Li
  In AAAI, 2019.
  [bib] [pdf] [code]

Technical Blogs

• [23/June/2025] Entropy Maximization Alone Can Improve LLM Reasoning Performance? [Notion]