About Me

I'm a researcher at Shanghai Artificial Intelligence Laboratory. I received my PhD degree from Computer Science and Technology, Nanjing University in December 2025, advised by Professor Yang Gao. Before my PhD, I obtained my Bachelor degree from Harbin Institute of Technology, Shenzhen in 2020. My research interest includes reinforcement learning, multi-agent system and optimization.

🔥 News

• 2026.04: 🎉🎉 One paper is accepted to ACL 2026.
• 2026.01: 🎉🎉 One paper is accepted to ICLR 2026.
• 2025.11: 🎉🎉 Two papers are accepted to AAAI 2026.
• 2025.09: 🎉🎉 Two papers are accepted to NeurIPS 2025.
• 2025.05: 🎉🎉 One paper is accepted to ICML 2025.
• 2022.11: 🎉🎉 One paper is accepted to AAAI 2023.

📝 Selected Publications (*Equal Contribution, #Corresponding Author)

• Siyuan Gan, Jiaheng Liu, Boyan Wang, Tianpei Yang, Runqing Miao, Yuyao Zhang, Fanyu Meng, Junlan Feng, Linjian Meng#, Jing Huo#, Yang Gao. Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning. In: ACL 2026.
• Linjian Meng, Youzhi Zhang, Shangdong Yang, Wenbin Li, Tianyu Ding, Yang Gao. A Faster Parameter-Free Regret Matching Algorithm. In: ICLR 2026.
• Linjian Meng, Tianpei Yang, Youzhi Zhang, Zhenxing Ge, Yang Gao. Faster game solving via asymmetry of step sizes. In: AAAI 2026 (Oral Presentation, top 4~5%).
• Shuxin Zhuang*, Linjian Meng*, Shuxin Li, Minming Li, Youzhi Zhang. Tree-based stochastic optimization for solving large-scale urban network security games. In: AAAI 2026.
• Linjian Meng, Youzhi Zhang, Zhenxing Ge, Tianyu Ding, Shangdong Yang, Zheng Xu, Wenbin Li, Yang Gao. Last-Iterate Convergence of Smooth Regret Matching+ Variants in Learning Nash Equilibria. In: NeurIPS 2025.
• Linjian Meng, Tianpei Yang, Youzhi Zhang, Zhenxing Ge, Shangdong Yang, Tianyu Ding, Wenbin Li, Bo An, Yang Gao. Efficient last-iterate convergence in solving extensive-form games. In: NeurIPS 2025.
• Linjian Meng, Wubing Chen, Wenbin Li, Tianpei Yang, Youzhi Zhang, Yang Gao. Reducing variance of stochastic optimization for approximating Nash equilibria in normal-form games. In: ICML 2025 (Spotlight Poster, top 2.6%).
• Linjian Meng, Zhenxing Ge, Pinzhuo Tian, Bo An, Yang Gao. An Efficient Deep Reinforcement Learning Algorithm for Solving Imperfect Information Extensive-Form Games. In: AAAI 2023 (Oral Presentation, top 10.8%).