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清华叉院教授手把手教你用强化学习训练智能体
机器之心· 2025-08-19 02:43
在大模型智能体(Agent)时代,最重要的技术之一就是通过智能体强化学习(Agentic RL)训练通用智能体。ASearcher 是 AReaL 团队的第一个 Agentic RL 项 目,基于 AReaL 的全异步 Agentic RL,打造端到端搜索智能体(Search Agent)。 AReaL 允许智能体进行至多 128 次复杂环境交互;同时,极简代码设计让用户在单文件内就能实现复杂的长程工具调用(Long-Horizon Tool Use)。 本次分享中,吴翼教授将带领 AReaL 团队&ASearcher 项目核心成员,以多轮搜索智能体(multi-turn search agent)为例,手把手教大家用最少的代码实现极速 Agentic RL 训练。 分享主题: 清华叉院教授手把手教你用强化学习训练智能体 分享摘要 : 直播前准备: 嘉宾简介: 直播时间 : 北京时间 8 月 21 日 19:30-20:30 直播预约: 本次直播设有 QA 环节,欢迎大家加群一起来聊。 1. Agentic RL 的难点:长程工具调用 2. ASearcher 项目:全异步 RL 解锁 Agent 长程工具调 ...
从 OpenAI 回清华,吴翼揭秘强化学习之路:随机选的、笑谈“当年不懂股权的我” | AGI 技术 50 人
AI科技大本营· 2025-06-19 01:41
Core Viewpoint - The article highlights the journey of Wu Yi, a prominent figure in the AI field, emphasizing his contributions to reinforcement learning and the development of open-source systems like AReaL, which aims to enhance reasoning capabilities in AI models [1][6][19]. Group 1: Wu Yi's Background and Career - Wu Yi, born in 1992, excelled in computer science competitions and was mentored by renowned professors at Tsinghua University and UC Berkeley, leading to significant internships at Microsoft and Facebook [2][4]. - After completing his PhD at UC Berkeley, Wu joined OpenAI, where he contributed to notable projects, including the "multi-agent hide-and-seek" experiment, which showcased complex behaviors emerging from simple rules [4][5]. - In 2020, Wu returned to China to teach at Tsinghua University, focusing on integrating cutting-edge technology into education and research while exploring industrial applications [5][6]. Group 2: AReaL and Reinforcement Learning - AReaL, developed in collaboration with Ant Group, is an open-source reinforcement learning framework designed to enhance reasoning models, providing efficient and reusable training solutions [6][19]. - The framework addresses the need for models to "think" before generating answers, a concept that has gained traction in recent AI developments [19][20]. - AReaL differs from traditional RLHF (Reinforcement Learning from Human Feedback) by focusing on improving the intelligence of models rather than merely making them compliant with human expectations [21][22]. Group 3: Challenges in AI Development - Wu Yi discusses the significant challenges in entrepreneurship within the AI sector, emphasizing the critical nature of timing and the risks associated with missing key opportunities [12][13]. - The evolution of model sizes presents new challenges for reinforcement learning, as modern models can have billions of parameters, necessitating adaptations in training and inference processes [23][24]. - The article also highlights the importance of data quality and system efficiency in training reinforcement learning models, asserting that these factors are more critical than algorithmic advancements [30][32]. Group 4: Future Directions in AI - Wu Yi expresses optimism about future breakthroughs in AI, particularly in areas like memory expression and personalization, which remain underexplored [40][41]. - The article suggests that while multi-agent systems are valuable, they may not be essential for all tasks, as advancements in single models could render multi-agent approaches unnecessary [42][43]. - The ongoing pursuit of scaling laws in AI development indicates that improvements in model performance will continue to be a focal point for researchers and developers [26][41].