I did my Ph.D. at the Conversational AI Group, Department of Computer Science and Technology, Tsinghua University. I’m fortunate to be advised by Prof. Minlie Huang.

My interests are in natural language processing and deep learning. I am particularly interested in how we can build a robust and scalable AI system that can leverage diverse skills (e.g., tool use and reasoning) to aggregate possibly-heterogeneous information and answer natural language questions precisely regardless of their complexity.

Research Highlights

LLM Reasoning & Tool Augmentation

• Informal Math Pre-Training and (large-scale) RL: DeepSeekMath project demonstrates an effective data engineering pipeline for math pre-training, and lays the GRPO-based RL foundation for post-training DeepSeek models. The DeepSeek-R1 project further leverages large-scale RL to create a strong reasoning model that approaches OpenAI’s o1 performance in many reasoning tasks;
• Formal Math Data Synthesis and Proof Search: The DeepSeek-Prover project improves formal math reasoning (i.e., to generate math proofs that can be automatically verified) with large-scale expert iteration (DeepSeek-Prover), RL from proof assistant’s feedback (DeepSeek-Prover-V1.5), and tree search (DeepSeek-Prover-V1.5);
• Reasoning with Tool Integration: The ToRA project augments chain-of-thought reasoning with Python code for strong math performance. The Critic project experiments on more general reasoning tasks to study self-correction based on feedback from tools.

Publications

• Huajian Xin, Z.Z. Ren, Junxiao Song, Zhihong Shao, DeepSeek-AI
  DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search
  International Conference on Learning Representations (ICLR), 2025.
  [paper]

• Huajian Xin, Daya Guo, Zhihong Shao, Zhizhou Ren, Qihao Zhu, Bo Liu, Chong Ruan, Wenda Li, Xiaodan Liang
  DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data
  The Annual Conference on Neural Information Processing Systems (NeurIPS), MATH-AI workshop, 2024.
  [paper]

• Peiyi Wang, Lei Li, Zhihong Shao, R.X. Xu, Damai Dai, Yifei Li, Deli Chen, Y.Wu, Zhifang Sui
  Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
  The Annual Meeting of the Association for Computational Linguistics (ACL), 2024.
  [paper]

• Jiaxin Wen, Ruiqi Zhong, Pei Ke, Zhihong Shao, Hongning Wang, Minlie Huang
  Assisting Humans For Scalable Oversight by Learning Decomposition From Human Feedback: A Case Study in Competitive Programming
  The Annual Meeting of the Association for Computational Linguistics (ACL), 2024.
  [paper]

• Zhihong Shao, Zhibin Gou, Yeyun Gong, Yelong Shen, Yujiu Yang, Minlie Huang, Nan Duan, and Weizhu Chen
  ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving
  International Conference on Learning Representations (ICLR), 2024.
  [Paper]/[Code]
  (ToRA-34B is the first open-source model that attains an accuracy over 50% on the competition-level MATH dataset)

• Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen
  Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
  Findings of Empirical Methods in Natural Language Processing (Findings of EMNLP), 2023.
  [Paper]

• Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Nan Duan, and Weizhu Chen
  CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
  International Conference on Learning Representations (ICLR), 2024.
  [paper]/[code]

• Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen
  Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
  International Conference on Machine Learning (ICML), 2023.
  [Paper]

• Zhihong Shao, Fei Huang, Minlie Huang
  Chaining Simultaneous Thoughts for Numerical Reasoning
  Findings of Empirical Methods in Natural Language Processing (Findings of EMNLP), 2022.
  [Paper]

• Zhihong Shao, Minlie Huang
  Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework
  The Annual Meeting of the Association for Computational Linguistics (ACL), 2022.
  [Paper]/[Code]
  (Best QA system on the AmbigNQ leaderboard)

• Zhihong Shao, Zhongqin Wu, Minlie Huang
  AdvExpander: Generating Natural Language Adversarial Examples by Expanding Text
  Transactions on Audio, Speech, and Language Processing (TASLP), vol. 30, pp. 1184-1196, 2022.
  [Paper]

• Zhihong Shao, Lifeng Shang, Qun Liu, Minlie Huang
  A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering
  The Annual Meeting of the Association for Computational Linguistics (ACL), 2021.
  [Paper]/[Code]

• Zhihong Shao, Minlie Huang, Jiangtao Wen, Wenfei Xu, and Xiaoyan Zhu
  Long and Diverse Text Generation with Planning-based Hierarchical Variational Model
  Empirical Methods in Natural Language Processing (EMNLP), 2019.
  [Paper]/[Code]

Preprints

• DeepSeek-AI
  DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
  Arxiv abs/2501.12948, 2025.
  [paper]

• DeepSeek-AI
  DeepSeek-V3 Technical Report
  Arxiv abs/2412.19437, 2024.
  [paper]

• Qihao Zhu, Daya Guo, Zhihong Shao, Dejian Yang, DeepSeek-AI
  DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
  Arxiv abs/2406.11931, 2024.
  [paper]

• DeepSeek-AI
  DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
  Arxiv abs/2405.04434, 2024.
  [paper]

• Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Mingchuan Zhang, Y.K. Li, Y. Wu, Daya Guo
  DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
  Arxiv abs/2402.03300, 2024.
  [paper]/[code]

• DeepSeek-AI
  DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
  Arxiv abs/2401.02954, 2024.
  [paper]/[code]

• Fei Huang, Dazhen Wan, Zhihong Shao, Pei Ke, Jian Guan, Yilin Niu, Xiaoyan Zhu, and Minlie Huang
  CoTK: An Open-Source Toolkit for Fast Development and Fair Evaluation of Text Generation
  Arxiv abs/2002.00583, 2020.
  [Paper]/[Code]

Selected Honors and Awards

• Lenovo Scholarship, Tsinghua University, 2023
• 1nd Prize, Comprehensive Scholarship, Tsinghua University, 2022
• 2nd Prize, Comprehensive Scholarship, Tsinghua University, 2021
• 3rd Prize, the National Final of “LAN QIAO CUP” C/C++ Group, 2018
• China National Scholarship 2017
• 1st Prize, National College Students Mathematics Competition (non-math-major), 2016
• China National Scholarship, 2016

Services

Reviewer/Program Committee: ACL, EMNLP, NLPCC, ARR

Teaching

I was a TA for the following undergraduate courses:

• Artificial Neural Network (2019 Fall, 2020 Fall, 2021 Fall, 2022 Fall)
• Object-Oriented Programming (2020 Spring, 2021 Spring, 2022 Spring)