Core Insights - The article discusses AReaL-lite, a reinforcement learning training framework designed for algorithm developers, allowing users to modify a single file to implement various RL training algorithms and custom agent workflows, while achieving optimal model performance through Fully Async RL [1][10]. Group 1: Event Details - The sharing session will feature Professor Wu Yi from Tsinghua University's Interdisciplinary Information Institute and core members of the AReaL team, using a multi-turn math reasoning example to teach RL [2][10]. - The live session is scheduled for August 7, 19:30-20:30 Beijing time, and participants are encouraged to prepare a GPU server, preferably with 4 cards [8][10]. Group 2: AReaL-lite Features - AReaL-lite's key characteristics include: - Fully async RL for rapid training [10]. - Ecosystem-friendly, compatible with various open-source ecosystems [10]. - Algorithm-first approach, ensuring minimal file modifications for complex algorithms [10]. Group 3: Team Introduction - The team includes: - Wu Yi, Assistant Professor at Tsinghua University and Chief Scientist of the AReaL team [10]. - Fu Wei, a PhD student at Tsinghua University and core member of the AReaL project [10]. - Mei Zhiyu, a researcher at Ant Group's reinforcement learning lab and a PhD from Tsinghua University [10].