Core Viewpoint - The article discusses the significance of Agentic Reinforcement Learning (Agentic RL) in training general intelligent agents, highlighting the ASearcher project as a key initiative by the AReaL team to develop an end-to-end search agent using this technology [1][2]. Summary by Sections Agentic RL Challenges - The main difficulty in Agentic RL is the long-horizon tool usage, which requires complex interactions in various environments [11]. ASearcher Project - ASearcher leverages fully asynchronous RL to unlock long-horizon tool usage for agents, allowing up to 128 complex environment interactions [2][11]. AReaL-Lite - AReaL-Lite is introduced as a lightweight development framework that enables rapid training of Agentic RL, simplifying the coding process [11]. Hands-on Training - The article mentions a hands-on session where participants will learn to implement multi-turn search agent training in Jupyter Notebook, emphasizing the need for a GPU server with at least 4 cards [11]. Guest Speakers - The session features notable speakers including Professor Wu Yi from Tsinghua University and key members from the AReaL and ASearcher projects, highlighting their expertise in the field [11].