王锡淮Xihuai Wang
Shanghai, China
I received my Ph.D. from the APEX Lab at Shanghai Jiao Tong University in 2026, under the supervision of Prof. Weinan Zhang and Prof. Ying Wen, affiliated with both the SJTU-Apex Group and the SJTU-MARL Group. I was honored to be selected into the Wen-Tsun Wu AI Honorary Doctoral Program in 2020. Prior to this, I received my B.Eng. in Computer Science and Technology from the School of Computer Science and Engineering, Sun Yat-sen University in 2020.
My research lies at the intersection of Reinforcement Learning and Multi-Agent Learning. My current research focuses on:
- Large Language Model Reasoning and Agency
- Reinforcement learning methodologies for enhancing reasoning and agentic capabilities of LLMs
- Human-AI collaborative decision-making with LLM-based agents
- Multi-Agent Reinforcement Learning
- Sample-efficient algorithms for cooperative MARL
- Zero-shot coordination and generalization in multi-agent systems
| Jan 13, 2026 |
I have successfully defended my Ph.D. thesis and graduated from Shanghai Jiao Tong University! 🎉
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| Dec 2, 2025 |
A blog post sharing my perspective on KL estimators in reinforcement learning.
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| Nov 23, 2025 |
A blog post sharing my perspective on training–inference mismatch in reinforcement learning for large language models.
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| May 16, 2025 | Our paper Leveraging Dual Process Theory in Language Agent Framework for Real-time Simultaneous Human-AI Collaboration has been accepted to ACL 2025! |
| Sep 26, 2024 | Our work about zero-shot coordination evaluation ZSC-Eval is accepted by NeurIPS 2024 Dataset and Benchmark Track! |
| Aug 8, 2023 | Give a talk about cooperative multi-agent reinforcement learning (Coordinate Agents vis Policy Optimization) at RLChina |
| Mar 25, 2023 | Our work about policy optimization in cooperative multi-agent scenarios Order Matters: Agent-by-agent Policy Optimization is accepted by ICLR 2023! |
Full publications are available on Google Scholar or Publications (* denotes equal contribution). Works are organized by topic: LLM Reasoning and Agency, Multi-agent Reinforcement Learning.