I am a Research Scientist at DeepSeek working on LLM reasoning. I’m interested in building self-improving systems that can accomplish increasingly complex tasks by leveraging a variety of skills, such as tool use and reasoning. I was named one of MIT Tech Review’s 35 Innovators Under 35.

I received a PhD in computer science from Tsinghua University, advised by Prof. Minlie Huang.

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), tree search (DeepSeek-Prover-V1.5), and by bridging informal and formal reasoning with large-scale RL (DeepSeek-Prover-V2);
• 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

• DeepSeek-AI
  DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning
  Nature, vol. 645, pp. 633-638, 2025.
  [paper]

• 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

• Z.Z. Ren, Zhihong Shao, Junxiao Song, DeepSeek-AI
  DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition
  Arxiv abs/2504.21801, 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

• Area Chair: NeurIPS
• 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)