About Jing-Cheng Pang (庞竟成):
[Research Overview] [Recent News] [Selected Publications] [Projects]
I am a fourth-year Ph.D. candidate at Nanjing University, where I study reinforcement learning under the guidance of Professor Yang Yu. In June 2019, I obtained B.Sc. degree from UESTC, after which I joined the LAMDA group led by Professor Zhi-Hua Zhou, as a Master’s student. In 2021, I transitioned to the Successive Postgraduate and Doctoral Program to pursue my current doctoral research. From July to October in 2024, I engaged as a visiting scholar with Prof. Masashi Sugiyama’s team at RIKEN-AIP, Tokyo, Japan.
Research Overview
My research focuses on connecting human and intelligent agent through natural language. By integrating reinforcement learning (RL) and large language models (LLMs), I aim to develop systems that not only interpret human intent but also act autonomously and learn iteratively in dynamic environments. Particularly, my study includes:
- Reinforcement Learning: language-conditioned RL, optimization algorithm, imitation learning, generalist agent;
- Large Language Models: model training, inference-time optimization, LLM-based agent;
- Embodied AI: home-service robot, sim2real policy learning.
Free free to contact/follow me if you are interested in my work.
Recent News
- 2025.03: Invited talk at HUAWEI Jiaxian Community (稼先社区). Topic: Exploration on Intelligent Agent System based on RL and LLMs [slides].
- 2025.01: ReViWo for robotics manipulation under viewpoint disturbance, is accepted by ICLR 2025.
- 2025.01: Invited talk at HUAWEI NAIE Group. Topic: RL-driven LLM Optimization and Recent Progress [slides].
- 2024.12: InCLET is accepted by AAMAS 2025 as a full paper.
We will give an oral presentation at Detriot(It’s a pity that we can not get the visa)! - 2024.09: Our work, KALM, is accepted by NeurIPS 2024!
- 2024.07: I start my visiting to Prof. Masashi Sugiyama’s team at RIKEN-AIP, Tokyo, Japan, doing RL research
- 2024.01: RLC for LLM self-improvement is accepted by ICLR 2024
- 2023.11: Awarded as Top Reviewer (8%) of NeurIPS 2023
- 2023.09: TALAR, for instruction-following agents, gets accepted by NeurIPS 2023
Selected Publications
- Jing-Cheng Pang, Nan Tang, Kaiyuan Li, Yuting Tang, Xin-Qiang Cai, Zhen-Yu Zhang, Gang Niu, Masashi Sugiyama and Yang Yu. Learning View-invariant World Models for Visual Robotic Manipulation. In: ICLR, 2025. [paper]
- Peng-Yuan Wang, Jing-Cheng Pang, Chen-Yang Wang, Xu-Hui Liu, Tian-Shuo Liu, Si-Hang Yang, Hong Qian and Yang Yu. InCLET: In-context Learning from Language Models can Improve Embodied Instruction-following. In: AAMAS (Oral), 2025. [paper]
- Jing-Cheng Pang, Si-Hang Yang, Kaiyuan Li, Jiaji Zhang, Xiong-Hui Chen, Nan Tang and Yang Yu. KALM: Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts. In: NeurIPS, 2024. [paper]
- Jing-Cheng Pang, Peng-Yuan Wang, Kaiyuan Li, Xiong-Hui Chen, Jiacheng Xu, Zongzhang Zhang and Yang Yu. Language Model Self-improvement by Reinforcement Learning Contemplation. In: ICLR, 2024. [paper]
- Jing-Cheng Pang, Xinyu Yang, Si-Hang Yang, Xiong-Hui Chen and Yang Yu. Natural Language Instruction-following with Task-related Language Development and Translation. In: NeurIPS, 2023. [paper]
- Jing-Cheng Pang, Tian Xu, Shengyi Jiang, Yu-Ren Liu and Yang Yu. Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization. TNNLS, to appear. [paper]
- Shengyi Jiang, Jing-Cheng Pang and Yang Yu. Offline Imitation Learning with a Misspecified Simulator. In: NeurIPS, 2020. [paper]
Projects
- Awesome-Papers-Autonomous-Agent
A collection of recent papers on building autonomous agent. Two topics included: RL-based / LLM-based agents. - ImagineBench
A benchmark for evaluating RL algorithms that train the policies using both real data and imaginary rollouts from LLMs.
