Core Insights - The article discusses the transition of large models into the second half of reinforcement learning (RL), emphasizing the importance of redefining problems and designing real-use case evaluations [1] - It highlights the need for effective measurement of the ROI of agent products, particularly for startups and enterprises looking to leverage AI [1] - Superclue has launched a new evaluation benchmark, AgentCLUE-General, which deeply analyzes the capabilities of mainstream agent products [1] Group 1 - The blog post by OpenAI's Agent Researcher, Yao Shunyu, has sparked discussions on the shift from model algorithms to practical utility [1] - The evaluation framework for agent products is crucial for guiding product development and implementation in enterprises [1] - Superclue maintains close connections with various model and agent teams, showcasing its expertise in model evaluation [1] Group 2 - An online sharing session is scheduled for May 15, from 20:00 to 22:00, with limited slots available for registration [2] - The article suggests that understanding how agents can be implemented in enterprises is a key area of interest [3] - It raises questions about the differences in capabilities among various general agent products, such as Manus, Fellou, and Genspark [3]
下周聊:大模型进入 RL 下半场,模型评估为什么重要?
Founder Park·2025-05-09 11:55