Claude 4 核心成员访谈：提升 Agent 独立工作能力，强化模型长程任务能力是关键

Core Insights - The main change expected in 2025 is the effective application of reinforcement learning (RL) in language models, particularly through verifiable rewards, leading to expert-level performance in competitive programming and mathematics [4][6][7]. Group 1: Reinforcement Learning and Model Development - Reinforcement learning has activated existing knowledge in models, allowing them to organize solutions rather than learning from scratch [4][11]. - The introduction of Opus 4 has significantly improved context management for multi-step actions and long-term tasks, enabling models to perform meaningful reasoning and execution over extended periods without frequent user intervention [4][32]. - The current industry trend prioritizes computational power over data and human feedback, which may evolve as models become more capable of learning in real-world environments [4][21]. Group 2: Future of AI Agents - The potential for AI agents to automate intellectual tasks could lead to significant changes in the global economy and labor market, with predictions of "plug-and-play" white-collar AI employees emerging within the next two years [7][9]. - The interaction frequency between users and models is expected to shift from seconds and minutes to hours, allowing users to manage multiple models simultaneously, akin to a "fleet management" approach [34][36]. - The development of AI agents capable of completing tasks independently is anticipated to accelerate, with models expected to handle several hours of work autonomously by the end of the year [36][37]. Group 3: Model Capabilities and Limitations - Current models still lack self-awareness in the philosophical sense, although they exhibit a form of meta-cognition by expressing uncertainty about their answers [39][40]. - The models can simulate self-awareness but do not possess a continuous identity or memory unless explicitly designed with external memory systems [41][42]. - The understanding of model behavior and decision-making processes is still evolving, with ongoing research into mechanisms of interpretability and the identification of features that drive model outputs [46][48]. Group 4: Future Developments and Expectations - The frequency of model releases is expected to increase significantly, with advancements in reinforcement learning leading to rapid improvements in model capabilities [36][38]. - The exploration of long-term learning mechanisms and the ability for models to evolve through practical experience is a key area of focus for future research [30][29]. - The ultimate goal of model interpretability is to establish a clear understanding of how models make decisions, which is crucial for ensuring their reliability and safety in various applications [46][47].