🚀 I am a penultimate year PhD student at the University of Cambridge, supervised by Prof. Mihaela van der Schaar. During my M.Phil. study at MMLab@CUHK, I was advised by Prof. Dahua Lin and Prof. Bolei Zhou; I received my BSc in Physics from the Yuanpei Honor Program, at Peking University, and a BSc from the Guanghua School of Management, at Peking University. My undergrad thesis was advised by Prof. Zhouchen Lin.

🤖️ I believe Reinforcement Learning is a vital component of the solution for achieving AGI. My previous work on deep reinforcement learning is motivated by reality-centric applications like robotics🦾, healthcare💉, finance📈, and large language models🧠. My research keywords during the past years include:

• RL in Language Models. (2023-); Interpretable RL (2023-); Inverse RL (2021-);
• Uncertainty Quantification (2022-); Data-Centric Off-Policy Evaluation (2022-);
• Value-Based Deep-RL (2021-); Offline RL (2021-); Optimism in Exploration (2021-);
• Continuous Control via Supervised Learning (2020-); Goal-Conditioned RL (2020-)
• RL in Robotics (2019-)

🤝 I’m open to collaborations. Please drop me an email if you find my work interesting. Let us push RL closer to genuine general intelligence!

News

📄 (2024.03) I wrote an article arguing that Supervised Fine Tuning is Inverse Reinforcement Learning!
💬 (2024.03) Prompt-OIRL and RATP are featured at the Inspiration Exchange, recording is online .
📄 (2024.02) 2 RL+LLM papers are online! ABC uses the attention mechanism to solve the credit assignment problem in RLHF; RATP uses MCTS to enhance the reasoning ability of LLMs with external documents
📄 (2024.01) 1 RL+LLM paper Prompt-OIRL is accepted by ICLR 2024! It uses Inverse RL to Evaluate and Optimize Prompts for LLMs
💬 (2024.01) Invited talk on RLHF at the Intuit AI Research Forum. slide
💬 (2023.12) Invited talk on RLHF at the Likelihood Lab. Discussion on Nash-Learning from Human Feedback is included! slide
💬 (2023.11) Invited talk on RLHF at the CoAI group, THU. Discussion on the problems of the Bradley-Terry Model is included. slide
📄 (2023.10) Our paper Prompt-OIRL is selected as an oral presentation at the NeurIPS 2023 ENLSP workshop!
📄 (2023.10) I wrote an article on RLHF to share my thoughts as an RL researcher in the Era of LLMs.
📄 (2023.9) 2 papers on Interpretable Offline RL and Interpretable Uncertainty Quantification are accepted by NeurIPS 2023.
💬 (2023.9) Invited talk on “Reinforcement Learning in the Era of LLMs” at Kuaishou Research. slide is online
📄 (2023.2) 2 papers are accepted by AISTATS 2023.
💬 (2022.11) Invited talk on value-based DRL at HW Cloud Research. slide is online
📄 (2022.9) 1 paper on Value-Based DeepRL is accepted by NeurIPS 2022. 2 papers are presented at the FMDM workshop, and 2 papers are presented at the DeepRL workshop.
📄 (2022.1) 1 paper on Offline GCRL is accepted by ICLR 2022.