Publications

2026

1. ACL Findings 2026

   Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback

   Jiaye Lin ,  Mengdi Li ,  Xufeng Zhao , and 4 more authors

   2026

   ACL 2026 Findings

   arXiv Bib

   @misc{Lin26CurriculumRLAIF,
     title = {Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback},
     author = {Lin, Jiaye and Li, Mengdi and Zhao, Xufeng and Lu, Wenhao and Zhao, Peilin and Wermter, Stefan and Wang, Di},
     year = {2026},
     url = {https://arxiv.org/abs/2505.20075},
     note = {ACL 2026 Findings},
   }

2025

1. TMLR 2025

   Mental Modeling of Reinforcement Learning Agents by Language Models

   Wenhao Lu ,  Xufeng Zhao ,  Josua Spisak , and 2 more authors

   Transactions on Machine Learning Research, 2025

   Also presented at the 18th European Workshop on Reinforcement Learning

   Abs arXiv Bib Website

   This work studies whether language models can infer and reason about reinforcement learning agents from observed interaction histories.

   @article{Lu25MentalModeling,
     title = {Mental Modeling of Reinforcement Learning Agents by Language Models},
     author = {Lu, Wenhao and Zhao, Xufeng and Spisak, Josua and Lee, Jae Hee and Wermter, Stefan},
     journal = {Transactions on Machine Learning Research},
     year = {2025},
     url = {https://openreview.net/forum?id=JN7iNWaPTe},
     note = {Also presented at the 18th European Workshop on Reinforcement Learning},
   }

2024

1. 

   Causal State Distillation for Explainable Reinforcement Learning

   Wenhao Lu ,  Xufeng Zhao ,  Thilo Fryen , and 4 more authors

   In 3rd Conference on Causal Learning and Reasoning (CLeaR 2024) , Apr 2024

   Oral presentation

   Abs arXiv Bib Website

   This work extends reward decomposition with causal representation objectives to provide more informative local explanations for reinforcement learning agents.

   @inproceedings{Lu24CausalState,
     title = {Causal {{State Distillation}} for {{Explainable Reinforcement Learning}}},
     author = {Lu, Wenhao and Zhao, Xufeng and Fryen, Thilo and Lee, Jae Hee and Li, Mengdi and Magg, Sven and Wermter, Stefan},
     booktitle = {3rd Conference on Causal Learning and Reasoning (<b>CLeaR 2024</b>)},
     year = {2024},
     month = apr,
     url = {https://proceedings.mlr.press/v236/lu24a.html},
     note = {Oral presentation},
   }

2. COLING 2024

   Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic

   Xufeng Zhao ,  Mengdi Li ,  Wenhao Lu , and 4 more authors

   In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) , May 2024

   Oral presentation

   Bib

   @inproceedings{Zhao24LogicThoughts,
     title = {Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic},
     author = {Zhao, Xufeng and Li, Mengdi and Lu, Wenhao and Weber, Cornelius and Lee, Jae Hee and Chu, Kun and Wermter, Stefan},
     booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (<b>LREC-COLING 2024</b>)},
     year = {2024},
     month = may,
     pages = {6144--6166},
     publisher = {ELRA and ICCL},
     url = {https://aclanthology.org/2024.lrec-main.543/},
     note = {Oral presentation},
   }

3. ICANN 2024

   Details Make a Difference: Object State-Sensitive Neurorobotic Task Planning

   Xiaowen Sun ,  Xufeng Zhao ,  Jae Hee Lee , and 3 more authors

   In Artificial Neural Networks and Machine Learning - ICANN 2024 , May 2024

   arXiv Bib

   @inproceedings{Sun24ObjectStateSensitive,
     title = {Details Make a Difference: Object State-Sensitive Neurorobotic Task Planning},
     author = {Sun, Xiaowen and Zhao, Xufeng and Lee, Jae Hee and Lu, Wenhao and Kerzel, Matthias and Wermter, Stefan},
     booktitle = {Artificial Neural Networks and Machine Learning - {{ICANN}} 2024},
     series = {Lecture Notes in Computer Science},
     pages = {261--275},
     publisher = {Springer},
     year = {2024},
     doi = {10.1007/978-3-031-72341-4_18},
     url = {https://doi.org/10.1007/978-3-031-72341-4_18},
   }

4. Humanoids 2024

   Large Language Models for Orchestrating Bimanual Robots

   Kun Chu ,  Xufeng Zhao ,  Cornelius Weber , and 3 more authors

   In 23rd IEEE-RAS International Conference on Humanoid Robots (Humanoids 2024) , May 2024

   arXiv Bib

   @inproceedings{Chu24Labor,
     title = {Large Language Models for Orchestrating Bimanual Robots},
     author = {Chu, Kun and Zhao, Xufeng and Weber, Cornelius and Li, Mengdi and Lu, Wenhao and Wermter, Stefan},
     booktitle = {23rd {{IEEE-RAS}} International Conference on Humanoid Robots (<b>Humanoids 2024</b>)},
     pages = {328--334},
     publisher = {IEEE},
     year = {2024},
     doi = {10.1109/HUMANOIDS58906.2024.10769891},
     url = {https://doi.org/10.1109/Humanoids58906.2024.10769891},
   }

2023

1. ICDL 2023

   A Closer Look at Reward Decomposition for High-Level Robotic Explanations

   Wenhao Lu ,  Xufeng Zhao ,  Sven Magg , and 3 more authors

   In IEEE International Conference on Development and Learning (ICDL 2023) , Nov 2023

   Oral presentation

   Abs arXiv Bib

   This work combines reward decomposition with abstract action spaces to generate high-level explanations for reinforcement learning agents in robotic scenarios.

   @inproceedings{Lu23CloserLook,
     title = {A {{Closer Look}} at {{Reward Decomposition}} for {{High-Level Robotic Explanations}}},
     author = {Lu, Wenhao and Zhao, Xufeng and Magg, Sven and Gromniak, Martin and Li, Mengdi and Wermter, Stefan},
     booktitle = {IEEE International Conference on Development and Learning (<b>ICDL 2023</b>)},
     year = {2023},
     month = nov,
     doi = {10.48550/arXiv.2304.12958},
     url = {https://arxiv.org/abs/2304.12958},
     note = {Oral presentation},
   }