Wanpeng Zhang (张万鹏)

I am a Ph.D. candidate in the School of Computer Science at Peking University, advised by Prof. Zongqing Lu. My research interests include Foundation Models, Embodied AI, and Reinforcement Learning. Feel free to contact me if you are interested in discussing or collaborating. For more details, please refer to my CV or CV(Chinese).

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Selected Publication

(For the full publications, please see my Google Scholar.)

1. Embodied AI

• (arXiv’25.07) Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos
  • Hao Luo*, Yicheng Feng*, Wanpeng Zhang*, Sipeng Zheng*, Ye Wang, Haoqi Yuan, Jiazheng Liu, Chaoyi Xu, Qin Jin, Zongqing Lu. *Equal Contribution.
  • TLDR: We introduce Being-H0, the first dexterous Vision-Language-Action model pretrained from large-scale human videos via explicit hand motion modeling.
  • Project / Paper / Bib / GitHub / Hugging Face

2. MLLM

• (NeurIPS’25) OpenMMEgo: Enhancing Egocentric Understanding for LMMs with Open Weights and Data.
  • Hao Luo, Zihao Yue, Wanpeng Zhang, Yicheng Feng, Sipeng Zheng, Deheng Ye, Zongqing Lu.
  • TLDR: OpenMMEgo enhances egocentric video understanding through a multi-level synthetic dataset, semantic-aware visual token compression to handle viewpoint shifts, and curriculum learning for stable training.
  • Paper / Bib / GitHub
• (ICCV’25, Highlight) Unified Multimodal Understanding via Byte-Pair Visual Encoding.
  • Wanpeng Zhang, Yicheng Feng, Hao Luo, Yijiang Li, Zihao Yue, Sipeng Zheng, Zongqing Lu.
  • TLDR: Building upon the visual BPE Tokenizer proposed in the previous work, we further designed a complete training framework and our Being-VL-0.5 model.
  • Project / Paper / Bib / GitHub / Link
• (ICCV’25) VideoOrion: Tokenizing Object Dynamics in Videos.
  • Yicheng Feng, Yijiang Li, Wanpeng Zhang, Hao Luo, Zihao Yue, Sipeng Zheng, Zongqing Lu.
  • TLDR: VideoOrion encodes videos with a two-branch design, using object tokens from a detect-segment-track pipeline to capture object dynamics alongside scene context.
  • Paper / Bib / Link
• (ICLR’25) From Pixels to Tokens: Byte-Pair Encoding on Quantized Visual Modalities.
  • Wanpeng Zhang, Zilong Xie, Yicheng Feng, Yijiang Li, Xingrun Xing, Sipeng Zheng, Zongqing Lu.
  • TLDR: We propose BPE Tokenizer for images, enabling Transformers to learn and align multi-modal information more effectively, providing a new learning paradigm for Unified MLLMs.
  • Paper / Bib / GitHub / Link

3. RL & Agent

• (NAACL’25) LLM-Based Explicit Models of Opponents for Multi-Agent Games.
  • Xiaopeng Yu, Wanpeng Zhang, Zongqing Lu.
  • TLDR: We propose EMO, a method that models each opponent individually using LLMs with iterative self- and global-refinement for better multi-agent reasoning.
  • Paper / Bib / Link
• (ICML’24) Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation.
  • Wanpeng Zhang, Yilin Li, Boyu Yang, Zongqing Lu.
  • TLDR: By adaptively learning the causal relationship joint graph in the environment and providing representations with causal relationships, RL algorithms can effectively tackle non-stationarities.
  • Paper / Bib / GitHub / Link
• (NAACL’24) AdaRefiner: Refining Decisions of Language Models with Adaptive Feedback.
  • Wanpeng Zhang, Zongqing Lu.
  • TLDR: We propose AdaRefiner to achieve the co-learning of LLMs and RL agents by enabling them to provide feedback to each other, optimizing both perception and decision-making capabilities.
  • Paper / Bib / GitHub / Link
• (ICML’23) Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning.
  • Ziluo Ding*, Wanpeng Zhang*, Junpeng Yue, Xiangjun Wang, Tiejun Huang, Zongqing Lu. *Equal Contribution.
  • TLDR: We propose EnDi framework, achieving agent goal division and collaboration enhancement in multi-agent systems through language and entity binding.
  • Paper / Bib / GitHub / Link
• (NeurIPS’22) Model-Based Opponent Modeling.
  • Xiaopeng Yu, Jiechuan Jiang, Wanpeng Zhang, Haobin Jiang, Zongqing Lu.
  • TLDR: MBOM uses environment models to recursively simulate and mix imagined opponent policies for adaptive opponent modeling.
  • Paper / Bib / GitHub / Link

Education

• Peking University. (Sep. 2022 — Jun. 2026 (Expected), Beijing, China)
  • Ph.D. Candidate in Computer Science.
  • Research Interest: Foundation Models / Embodied AI / Reinforcement Learning
• Tsinghua University. (Sep. 2019 — Jun. 2022, Beijing, China)
  • M.S. in Computer Science.
  • Research Interest: Reinforcement Learning
• Nankai University. (Sep. 2015 — Jun. 2019, Tianjin, China)
  • B.S. in Applied Mathematics.
  • Research Interest: Applied Mathematics / Machine Learning

Work Experience

• BeingBeyond. (Mar. 2025 — Present, Beijing, China)
  • Startup Team Member.
  • Foundation Models / VLA / Embodied AI
• Beijing Academy of Artificial Intelligence. (May. 2024 — Mar.2025, Beijing, China)
  • Research Scientist Intern.
  • Foundation Models / VLM / Embodied AI
• Tencent AI Lab
  • Research Scientist Intern. (Jun. 2020 — Jul. 2021, Shenzhen, China)
  • Reinforcement Learning

Patent

• Multimodal data processing method, device, storage medium, and electronic equipment. (CN119226992B)
  • Zongqing Lu, Wanpeng Zhang.
  • Link / PDF / Certificate
• Method, device and equipment for determining parameters and storage medium. (CN112527104A)
  • Wanpeng Zhang, Dijun Luo, Xi Xiao.
  • Link / PDF

Award

• National Scholarship. (2025)
• Merit Student of Peking University. (2025)
• Presidential Scholarship of Peking University. (2024)
• Award for Scientific Research of Peking University. (2024)
• Rhino-bird Elite Training Program of Tencent AI Lab. (2021)
• Mathematical Contest in Modeling (MCM/ICM), Meritorious Winner (First Prize). (2017)
• China Undergraduate Mathematical Contest in Modeling (CUMCM), Second Prize. (2016)
• National High School Mathematics Competition, Second Prize. (2014)

Service

• Conference Reviewer
  • ICML / NeurIPS / ICLR / ICCV / AAAI / ICRA / AISTATS
• Journal Reviewer
  • TNNLS / TIST / RAL
• Teaching Assistant
  • Deep Reinforcement Learning, Peking University. (Spring, 2025)