Hiš I am Zhouqi Hua (åę“²ē¦).
I am a first year PhD student at Fudan University, in the joint program in Large Model Center of Shanghai AI Laboratory. I am fortunate to be advised by Dr. Wenwei Zhang, Dr. Kai Chen and Prof. Dahua Lin. Before that, I received the bachelor degree at Tongji University in 2025 (under the supervision of Prof. Yufei Chen and Prof. Zhangkai Ni).
My research focus on generalization in LLMs, including length generalization and compositional generalization. Now I'm interested in investigating the mathematical abilities of LLMs.
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Shanghai AI LabResearch Intern @ Large Model Center
Joint Ph.D. StudentSep. 2025 - present -
Fudan UniversityPh.D. Student in Computer ScienceSep. 2025 - present -
Tongji UniversityB.S. in Computer ScienceSep. 2021 - Jul. 2025
Honors & Awards
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š„First Prize, National Intelligent Car Competition2024
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š„Second Prize, CCCC-MAIC (Hosted by Apple Inc.)2024
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š„Tongji Excellent Student Scholarship (First Prize)2024
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š„Tongji Excellent Student Scholarship (First Prize)2023
News
Selected Publications (view all )
Intern-S1: A Scientific Multimodal Foundation Model
Lei Bai, Zhongrui Cai, ..., Zhouqi Hua, ..., Yu Qiao et al.
Technical Report
Intern-S1 is a large multimodal MoE foundation model trained with massive scientific data and mixture-of-rewards reinforcement learning, achieving SOTA performance in scientific reasoning and professional tasks while remaining competitive in general reasoning among open-source models.
Intern-S1: A Scientific Multimodal Foundation Model
Lei Bai, Zhongrui Cai, ..., Zhouqi Hua, ..., Yu Qiao et al.
Technical Report
Intern-S1 is a large multimodal MoE foundation model trained with massive scientific data and mixture-of-rewards reinforcement learning, achieving SOTA performance in scientific reasoning and professional tasks while remaining competitive in general reasoning among open-source models.
The Imitation Game: Turing Machine Imitator is Length Generalizable Reasoner
Zhouqi Hua, Wenwei Zhang, Chengqi Lyu, Yuzhe Gu, Songyang Gao, Kuikun Liu, Dahua Lin, Kai Chen
International Conference on Learning Representations ICLR 2026
Turing Machine Imitation Learning (TAIL) is a synthetic chain-of-thought framework that instills Turing machineālike execution in LLMs, enabling robust length generalization for computable reasoning. On 18 challenging tasks, a 7B TAIL model outperforms the 671B DeepSeek-R1, establishing a new state of the art.
The Imitation Game: Turing Machine Imitator is Length Generalizable Reasoner
Zhouqi Hua, Wenwei Zhang, Chengqi Lyu, Yuzhe Gu, Songyang Gao, Kuikun Liu, Dahua Lin, Kai Chen
International Conference on Learning Representations ICLR 2026
Turing Machine Imitation Learning (TAIL) is a synthetic chain-of-thought framework that instills Turing machineālike execution in LLMs, enabling robust length generalization for computable reasoning. On 18 challenging tasks, a 7B TAIL model outperforms the 671B DeepSeek-R1, establishing a new state of the art.