Feng Chen
I am currently a Ph.D. candidate at the College of Computing and Data Science (CCDS), Nanyang Technological University (NTU), supervised by Prof. Bo An.
Previously, I obtained my M.Sc. and B.S. degrees from the School of Artificial Intelligence at Nanjing University, where I was advised by Prof. Zong-Zhang Zhang.
My research interests broadly include Reinforcement Learning, Multi-Agent Systems, and Large Language Model Agents.
Publications
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EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving
Advances in Neural Information Processing Systems (NeurIPS), 2025
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Learning to Reuse Policies in State Evolvable Environments
International Conference on Machine Learning (ICML), 2025
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Learning to Coordinate with Different Teammates via Team Probing
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2025 (In press)
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Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling
Annual Meeting of the Association for Computational Linguistics (ACL), 2025
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Efficient Multi-Agent Cooperation Learning through Teammate Lookahead
Transactions on Machine Learning Research (TMLR), 2025
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Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2025
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Stable Continual Reinforcement Learning via Diffusion-based Trajectory Replay
International Conference on Learning Representations (ICLR) Workshop GenAI4DM, 2024
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Efficient Human-AI Coordination via Preparatory Language-based Convention
International Conference on Learning Representations (ICLR) Workshop LLM Agents, 2024
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Dynamics Adaptive Safe Reinforcement Learning with a Misspecified Simulator
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2024
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Robust Multi-agent Communication via Multi-view Message Certification
Science China Information Sciences (SCIS), 2024
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Multi-Agent Policy Transfer via Task Relationship Modeling
Science China Information Sciences (SCIS), 2024
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Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation
Frontiers of Computer Science (FCS), 2024
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One by One, Continual Coordinating with Humans via Hyper-Teammate Identification
Transactions on Machine Learning Research (TMLR), 2024
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Learning to Coordinate with Anyone
International Conference on Distributed Artificial Intelligence (DAI), 2023 (Best Paper Award)
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Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers
AAAI Conference on Artificial Intelligence (AAAI), 2023 (Oral Presentation)
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Efficient Multi-agent Communication via Self-supervised Information Aggregation
Advances in Neural Information Processing Systems (NeurIPS), 2022
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Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract)
AAAI Conference on Artificial Intelligence (AAAI), 2023 (Student Abstract)
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Multi-Agent Concentrative Coordination with Decentralized Task Representation
International Joint Conference on Artificial Intelligence (IJCAI), 2022
Internship Experience
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Large Language Model Research Intern, ByteDance (Seed)Sep 2024 - Jun 2025Focused on algorithmic research and exploration during the post-training stage of Large Language Models. Investigated advanced optimization strategies and methodologies to enhance model performance and alignment.
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Reinforcement Learning Research Intern, ByteDanceJul 2021 - Oct 2021Conducted research within Game AI scenarios, specifically focusing on the training methodologies for intelligent agents and the exploration of algorithms for automated game scene generation.