在很多创业者眼里，自己凌晨在办公室里喝下一杯咖啡、随时关注不停闪烁的行业群、每15分钟看一次数据后 台，都是"为了抓住机会"的必要动作。 但可以试想一下，如果不做这些事情，你会不会极度不适应，并且感觉到焦虑和空虚？ 如果是的话，就说明你已经产生了心理学上说的"多巴胺戒断反应"。 或许是时候来一场"戒断"了——不是减少多巴胺的分泌，而是减少依赖刷手机、喝过量咖啡等"即时爽感"，将更 多的注意力转移到一些能带来长期影响的事情上来。例如，你可以从"早餐吃什么""怎么通勤"这些生活小事开 始，训练自己听见身体的信号，或者把"刷数据""盯融资"的即时刺激，转化到"解决用户问题""优化业务流程"等 重心上。最终，要尝试在"忙到脚不沾地"的创业节奏里，找到不依赖咖啡因、刷手机也能保持精力的状态。 这些行为本质，是多巴胺驱动的无效消 耗： 聚焦数据波动的短期刺激，可能会导致忽视用户体验等核心问 题；碎片化的信息接收，可能会让人没时间梳理出真正能落地的业务策略，最终陷入"越刷越焦虑，越焦虑 越想刷"的循环，把"盯业务"变成了"耗精力"。 有时候，创业者对多巴胺的依赖，会披着"为事业负责"的外衣——一些被解读为"高效""敬业"的 ...

Group 1 - The article discusses the transition from Supervised Fine-Tuning (SFT) to Reinforcement Learning (RL) in model training paradigms, highlighting that RL is becoming increasingly critical for enhancing model capabilities [3][4][8] - RL algorithms are evolving with new methods such as GRPO, RLOO, and DAPO, focusing on improving stability and sample efficiency [4] - The RL training process consists of three main modules: Rollout (policy generation), Reward Evaluation, and Policy Update, each playing a vital role in the training framework [5][6][7] Group 2 - The design of RL training frameworks faces challenges in coordinating Rollout and training modules, especially with the increasing model scale and the need for distributed multi-GPU training [12][13] - There is a diversity of underlying training and inference frameworks, which complicates parameter synchronization and inference scheduling [14] - Performance optimization strategies include data parallelism, tensor parallelism, and pipeline parallelism, each with distinct advantages and limitations [22][24] Group 3 - The article outlines the importance of efficient data transfer mechanisms and parameter synchronization between training frameworks and inference engines, emphasizing the need for flexible communication strategies [32][39] - SLIME and ROLL frameworks are introduced, showcasing their approaches to managing data transfer and parameter synchronization effectively [42][46] - The integration of Ray for distributed computing is discussed, highlighting its role in managing resource allocation and communication in complex RL tasks [48][53] Group 4 - The article concludes with a comparison of various RL frameworks, such as SLIME, ROLL, and Verl, each catering to different needs and offering unique features for specific applications [61] - The rapid evolution of technology necessitates maintaining simplicity and high maintainability in framework design to adapt to new trends [58] - The article emphasizes the significance of open-source frameworks in advancing RL technology, particularly in the context of China's leading position in technical strength and understanding [60]