Published several papers including 'Beyond Ten Turns: Unlocking Long-Horizon Agentic Search with Large-Scale Asynchronous RL', 'AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning', 'ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation'. Some works have been accepted by top conferences like ICML and ICLR.
Research Experience
Developing the AReaL project, which has accumulated 2.5k GitHub stars. Also involved in projects such as ASearcher and ReaLHF.
Education
PhD in Computer Science at Tsinghua University (expected graduation: June 2026), advised by Professor Yi Wu; BEng degree from the Department of Electrical Engineering, 2021.
Background
Research interests: the intersection of reinforcement learning (RL) and distributed systems. Currently focusing on developing AReaL — the fastest and easiest way to scale up agentic RL training for LLM/VLMs. Describes himself more as a programmer than a researcher.
Miscellany
Enjoys coding, cooking, listening to pop music, and working out at the gym.