Tonghe Zhang 张同和

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Blogs

Research

• ENPIRE: Agentic Robot Policy Self-Improvement in the Real World

  Wenli Xiao*, Jia Xie*, Tonghe Zhang*, Haotian Lin*, Letian "Max" Fu, Haoru Xue, Jalen Lu, Yi Yang, Cunxi Dai, Zi Wang, Jimmy Wu, Guanzhi Wang, S. Shankar Sastry, Ken Goldberg, Linxi "Jim" Fan, Yuke Zhu, Guanya Shi.

  Coding agents improve dexterous manipulation by automatic reset, eval, and training.

  CoRL 2026 · COLM WAB 2026 · COLM LSEI 2026

  Website arXiv Scholar
• BFM-Zero: A Promptable Behavioral Foundation Model for Humanoid Control
  Using Unsupervised Reinforcement Learning

  Yitang Li*^$, Zhengyi Luo*^$, Tonghe Zhang^$, Cunxi Dai^$, Anssi Kanervisto, Andrea Tirinzoni, Haoyang Weng, Kris Kitani, Mateusz Guzek, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta, Guanya Shi.

  Learn general humanoid motion with unsupervised RL.

  ICLR 2026

  Website arXiv GitHub Scholar

  @misc{li2025bfmzeropromptablebehavioralfoundation,
        title={BFM-Zero: A Promptable Behavioral Foundation Model for Humanoid Control Using Unsupervised Reinforcement Learning}, 
        author={Yitang Li and Zhengyi Luo and Tonghe Zhang and Cunxi Dai and Anssi Kanervisto and Andrea Tirinzoni and Haoyang Weng and Kris Kitani and Mateusz Guzek and Ahmed Touati and Alessandro Lazaric and Matteo Pirotta and Guanya Shi},
        year={2025},
        eprint={2511.04131},
        archivePrefix={arXiv},
        primaryClass={cs.RO},
        url={https://arxiv.org/abs/2511.04131}, 
  }

• ReinFlow: Fine-tuning Flow Matching Policy with Online Reinforcement Learning

  Tonghe Zhang, Chao Yu, Sichang Su, Yu Wang.

  Fine-tune flow matching policy/VLA with online RL, through noise injection.

  NeurIPS 2025

  Website arXiv Scholar GitHub WandB HuggingFace

  @misc{zhang2025reinflowfinetuningflowmatching,
        title={ReinFlow: Fine-tuning Flow Matching Policy with Online Reinforcement Learning}, 
        author={Tonghe Zhang and Chao Yu and Sichang Su and Yu Wang},
        year={2025},
        eprint={2505.22094},
        archivePrefix={arXiv},
        primaryClass={cs.RO},
        url={https://arxiv.org/abs/2505.22094}, 
  }

• π_RL: Online RL Fine-tuning for Flow-based Vision-Language-Action Models

  Kang Chen*, Zhihao Liu*, Tonghe Zhang*, Zhen Guo, Si Xu, Hao Lin, Hongzhi Zang, Quanlu Zhang, Zhaofei Yu, Guoliang Fan, Tiejun Huang, Yu Wang, Chao Yu.

  Fine-tune flow VLAs (π0.5, GR00T N1.5) with online RL in parallel simulation.

  Preprint

  arXiv GitHub HuggingFace Scholar

  @misc{chen2025pitextttrlonlinerlfinetuning,
        title={$\pi_\texttt{RL}$: Online RL Fine-tuning for Flow-based Vision-Language-Action Models},
        author={Kang Chen and Zhihao Liu and Tonghe Zhang and Zhen Guo and Si Xu and Hao Lin and Hongzhi Zang and Quanlu Zhang and Zhaofei Yu and Guoliang Fan and Tiejun Huang and Yu Wang and Chao Yu},
        year={2025},
        eprint={2510.25889},
        archivePrefix={arXiv},
        primaryClass={cs.LG},
        url={https://arxiv.org/abs/2510.25889},
  }

• 
  Sample-Efficient Reinforcement Learning of Flow Policies
  via Sequence Modeling

  Yixian Zhang*, Shu'ang Yu*, Tonghe Zhang, Mo Guang, Haojia Hui, Kaiwen Long, Yu Wang, Chao Yu, Wenbo Ding.

  Train flow matching policies with sample-efficient off-policy RL.

  ICLR 2025

  arXiv GitHub Website
• Think on Your Feet: Seamless Transition between Human-like Locomotions
  in Response to Changing Commands

  Huaxing Huang*, Wenhao Cui*, Tonghe Zhang*, Shengtao Li, Chenxu Hu, Tianchu Zhang, Liang Zheng, Ziyang Tang, Jinchao Han, Bangyu Qin, Tianchu Zhang, Shipu Zhang, Zheyuan Jiang.

  Learn smooth transitions between human-like humanoid locomotion skills.

  ICRA 2025

  IEEE Xplore arXiv Scholar

  @INPROCEEDINGS{11127948,
    author={Huang, Huaxing and Cui, Wenhao and Zhang, Tonghe and Li, Shengtao and Han, Jinchao and Qin, Bangyu and Zhang, Tianchu and Zheng, Liang and Tang, Ziyang and Hu, Chenxu and Zhang, Shipu and Jiang, Zheyuan},
    booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)}, 
    title={Think on Your Feet: Seamless Transition Between Human-Like Locomotion in Response to Changing Commands}, 
    year={2025},
    volume={},
    number={},
    pages={15965-15971},
    keywords={Adaptation models;Tracking;Imitation learning;Stability criteria;Humanoid robots;Multitasking},
    doi={10.1109/ICRA55743.2025.11127948}}

• 
  Provably Efficient Partially-observable Risk-sensitive Reinforcement Learning

  Tonghe Zhang*, Yu Chen*, Longbo Huang.

  Statistical theory for sample-efficient risk-sensitive RL under partial observation.

  ICML 2024

  PMLR arXiv Scholar GitHub

  @InProceedings{pmlr-v235-zhang24g,
    title = 	 {Provably Efficient Partially Observable Risk-sensitive Reinforcement Learning with Hindsight Observation},
    author =       {Zhang, Tonghe and Chen, Yu and Huang, Longbo},
    booktitle = 	 {Proceedings of the 41st International Conference on Machine Learning},
    pages = 	 {58680--58716},
    year = 	 {2024},
    editor = 	 {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
    volume = 	 {235},
    series = 	 {Proceedings of Machine Learning Research},
    month = 	 {21--27 Jul},
    publisher =    {PMLR},
    pdf = 	 {https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24g/zhang24g.pdf},
    url = 	 {https://proceedings.mlr.press/v235/zhang24g.html},
  }