Core Points - The meeting emphasized the importance of young cadres in learning and education, highlighting the need to understand the significance of the Central Committee's deployment and the long-term effectiveness of the Eight Regulations [1][2] - Zhao Yide stressed the necessity for young cadres to enhance their political qualities and to draw nourishment from Xi Jinping's thoughts, improving their political judgment, comprehension, and execution [2] Group 1 - Young cadres are the focus of the learning education, and they must recognize the serious nature of the "Four Winds" issues and implement educational tasks thoroughly [1] - The process of refining work style should be seen as a way to strengthen party character and enhance political awareness [2] - Young cadres should actively engage in key tasks and reforms, demonstrating a spirit of responsibility and problem-solving [2] Group 2 - The meeting called for a strategic focus on the cultivation of young cadres, emphasizing the need for strict education, management, and support [2] - It was highlighted that young cadres should maintain high moral standards and be vigilant against corruption, emphasizing the importance of family education and integrity [2]

Core Insights - The article outlines an eight-step roadmap for efficiently advancing in AI/ML by focusing on essential skills and avoiding common pitfalls [1]. Group 1: Step-by-Step Learning Path - **Step 1: Master Python Core Libraries** Proficiency in Python is essential for AI/ML, including data cleaning, model building, and result visualization [2]. Key content includes Python basics, advanced AI programming techniques, and libraries like scikit-learn, NumPy, Matplotlib, Seaborn, and Pandas [4]. Recommended resources include CS50 Python course and "Python Data Science Handbook" [4]. Suggested learning period is 3-4 weeks [4]. - **Step 2: Solidify Mathematical Foundations** A strong grasp of linear algebra, probability, and calculus is crucial for understanding models [5]. Key content includes matrix operations, Bayesian thinking, and optimization techniques [5]. Recommended resources include "Linear Algebra" by 3Blue1Brown and MIT's Probability Introduction [5]. Suggested learning period is 4-6 weeks [5]. - **Step 3: Understand Machine Learning Basics** This step is pivotal for transitioning from beginner to competent AI/ML engineer [6]. Key content includes supervised vs. unsupervised learning, reinforcement learning, and deep learning [6]. Recommended resources include Google's Machine Learning Crash Course and "Machine Learning" by Andrew Ng [8]. Suggested learning period is 6-8 weeks [8]. - **Step 4: Hands-On Project Experience** Practical experience through real AI/ML applications is essential for job readiness [9]. Key content includes practical guides and project development [9]. Suggested learning period is ongoing [9]. - **Step 5: Learn MLOps** Understanding MLOps is vital for deploying and maintaining models in real-world scenarios [10]. Key content includes foundational concepts and best practices for model deployment [10]. Suggested learning period is 3-4 weeks [10]. - **Step 6: Specialize in a Domain** After building a foundation, focusing on a specific area like NLP or computer vision enhances employability [11]. Suggested learning period is ongoing [11]. - **Step 7: Stay Updated** Continuous learning is necessary to keep skills relevant in the fast-evolving AI field [12]. Key resources include ArXiv for research papers and notable figures in the field [12]. Suggested learning period is ongoing [12]. - **Step 8: Prepare for Interviews** Comprehensive preparation for interviews is crucial, including explaining model principles and system design [13]. Recommended resources include machine learning interview guides [13]. Suggested learning period is 4-6 weeks [13]. Conclusion - The article emphasizes a structured approach to mastering AI/ML, enabling individuals to transition from novices to job-ready professionals efficiently [1].

"这哪行？暴雨来了，肯定得涝！"姜旺对张青云说，"必须抓紧按照排水渠的标准，挖深、拓宽、延 长。" "村里一时拿不出这么多钱……"张青云说。 "得要这么深，才不怕暴雨。"跳进排水渠，姜旺用身体作尺，量了量渠深。 "放心！这渠深1米、宽80厘米。"一旁的黄狮村党支部书记张青云说。 抢夏收、忙夏播、抓防汛，这段时间，河南省南阳市西峡县五里桥镇党委书记姜旺奔忙在田间地 头，"现在会议少了，有更多时间抓具体工作了。" 深入贯彻中央八项规定精神学习教育开展以来，西峡县积极推进减会议、减报表、优调研，让干部腾出 更多时间和精力，到一线办实事。 以精简会议为例，西峡县推行"多会合一"，将同类型的会议套开。以往，县农业农村局、水利局、交通 运输局、住房和城乡建设局都要召开防汛会议，今年全县安全生产、抗旱、防汛3个会议套开。今年以 来，西峡县召开各类会议数量同比下降35%，会议时长缩减50%以上。 减了会议，多出来的时间用在哪里？西峡县要求干部——"不能只在空调房里听汇报，必须直插基层一 线，去解决实际问题。" 来到黄狮村的猕猴桃园，姜旺发现，路旁只有一条灌溉用的水沟。 刚在这处检查完，又有村民找过来："修渠把路给弄断了，我们 ...

会议要求，市政府党组要切实扛牢主体责任，各级领导干部要坚持以身作则、以上率 下，带头入脑入心学、全面彻底查、注重实效改，全力营造风清气正、干事创业的良好政治 生态。 编辑：胡之澜 6月27日，市政府召开党组（扩大）会议，传达学习中央第三指导组指导督导湖北见面 会精神、省委党的建设工作领导小组会议暨深入贯彻中央八项规定精神学习教育推进会精 神，部署政府系统贯彻落实措施。市委副书记、市长、市政府党组书记盛阅春主持会议并讲 话。 会议指出，在全党开展深入贯彻中央八项规定精神学习教育，是今年党建工作的重点任 务。全市政府系统要切实把思想和行动统一到党中央决策部署和省委、市委工作要求上来， 进一步增强抓好学习教育的政治自觉、思想自觉、行动自觉，把接受指导督导作为践行"两 个维护"的政治检验、纵深推进全面从严治党的有力抓手，全力支持配合中央指导组开展工 作，确保学习教育取得实效。 会议强调，要在一体推进学查改上再加力再深化，对中央指导组指出的问题照单全收、 立行立改，确保学有质量、查有力度、改有成效。要以推动解决突出问题为重点，结合巡视 巡察、整治形式主义为基层减负、整治群众身边不正之风和腐败问题等重点工作，明确查摆 问 ...

Core Insights - The article discusses the potential of combining Reinforcement Learning (RL) with Large Language Models (LLMs), particularly focusing on the transition from post-training to pre-training phases, highlighting the challenges and opportunities in this area [2][3]. Group 1: Transition from Post-training to Pre-training - The integration of RL with LLMs is seen as a significant technological advancement, extending applications from post-training to pre-training phases [2]. - LLMs traditionally rely on supervised learning, which requires extensive and accurate human-provided data, making RL a viable alternative to address these limitations [3]. - RL's ability to generate data through model-environment interaction reduces the dependency on high-quality labeled data, thus lowering the requirements for supervision [3][4]. Group 2: Applications and Innovations in RL - Initial applications of RL in LLMs were focused on post-training, with techniques like Reinforcement Learning from Human Feedback (RLHF) being prominent [4]. - Recent advancements, such as Reinforcement Pre-Training (RPT) by researchers from Microsoft and Tsinghua University, have expanded RL's application to the pre-training phase, showing improved performance on certain benchmarks [4][5]. - RPT redefines the next token prediction (NTP) task as a verifiable reasoning task, potentially unlocking RL's capabilities while reducing reliance on labeled data [5]. Group 3: Challenges and Limitations - Despite the promising developments, the known limitations of RL in LLMs are still being uncovered, indicating that while the path appears bright, significant challenges remain [4][6]. - The training data and settings for RPT have yet to be validated across broader text and foundational models, and the computational resource demands for RL training continue to pose challenges [5].

整理 | 华卫 近日，据外媒报道，Meta 平台公司已招募四名前 OpenAI 研究人员加入其新成立的超级智能实验 室。 消息称，此次招聘对象包括 2022 年加入 ChatGPT 开发团队的特拉皮特·班萨尔（Trapit Bansal）。 据悉，他在启动 OpenAI 强化学习项目中发挥了关键作用。强化学习作为一种 AI 训练方法，适用于 构建推理模型。 另外三名已加入 Meta 的 OpenAI 研究人员分别是卢卡斯·拜尔（Lucas Beyer）、亚历山大·科列斯尼 科夫（Alexander Kolesnikov）和翟晓华（Xiaohua Zhai）。据了解，这三人曾于去年底协助建立 OpenAI 苏黎世办公室，此前他们在谷歌母公司 Alphabet 旗下的 DeepMind 机器学习实验室工作。 此次招聘发生在 Meta 首次披露组建超级智能研究团队的数周后。该实验室将负责开发能在广泛任务 中超越人类表现的 AI 模型。据悉，Meta 成立该部门的背景是其内部开发的大型语言模型 Llama 4 Behemoth 面临性能问题——该模型于今年早些时候预览，但因性能担忧已推迟发布。 上周，OpenAI 透 ...

今年来市场风格呈现出明显的大小盘分化，随着市场情绪的修复和市场活跃度的提升，大盘股表现相对较弱，而小盘股则受益于风险偏好提升、 流动性充沛等，表现尤为突出，量化策略的超额收益显著累积。 私募排排网数据显示，截至2025年5月底，有业绩显示的574只量化指增产品，近1年超额收益均值高达24.48%，其中正超额产品539只，正超额 占比高达93.91%。分三级策略来看，47只其他指增产品表现较为领先，近1年超额均值高达34.74%。（可参考： 最新量化多头超额榜揭晓！今 通、量创投资等领衔！进化论、龙旗、幻方等上榜！ ） 本文首发于公众号"私募排排网"。 （点击↑↑ 上图查看详情 ） 半鞅私募基金 ： 今年指数增强产品整体超额收益表现尤为突出，表观上来看，这得益于市场成交活跃度高、股票间的分化程度增加，这种市场 环境为量化管理人提供了丰富的交易机会，从而更容易获取超额收益。 从深层原因来看，则是在 固收类资产收益整体下行的背景下，权益市场因其相对较高的潜在回报和一定的"托底"效应，吸引了更多投资者的目 光，新增资金持续流入。 与此同时，特朗普上任后带来的市场不确定性增加，进一步激发了市场的波动性和交易活跃度。 最 ...

Core Viewpoint - Investing in health is essential for a prosperous future, emphasizing the importance of regular exercise, healthy eating, and annual health check-ups [1] Group 1: Health Investment - Engaging in a preferred sport three times a week and reducing processed food intake while increasing water consumption is recommended [1] - Annual health check-ups are crucial for early detection and adjustment of minor health issues [1] Group 2: Financial Management - Enhancing primary income through hard work and developing a habit of mandatory savings is advised [2] - A portion of income should be saved first, followed by learning basic financial management skills [2] Group 3: Continuous Learning - Investing in oneself by learning 1-2 new skills annually is highlighted as a highly beneficial investment [3] - This approach opens up more opportunities and reduces work-related stress, allowing for passive income growth [3] Group 4: Execution Strategies - Starting small and maintaining consistency is key; for instance, beginning with one workout per week or saving 5% of salary [5] - Finding enjoyment in activities, such as preferred sports or interesting skills, makes the process more engaging [5] Group 5: Regular Review - Monthly reviews of savings goals, exercise consistency, and family interactions are recommended [6] - Adjustments should be made if goals are not met, and small rewards should be given for progress [6] - Balancing effort with relaxation and enjoyment of life is essential for overall well-being [6] Group 6: Long-term Perspective - Achieving a fulfilling life at 35 is the result of daily mindful management, and taking action sooner leads to greater ease in life [7]

Core Viewpoint - The release of DeepSeek-R2 has been delayed due to CEO Liang Wenfeng's dissatisfaction with its performance and a shortage of Nvidia H20 chips, which are critical for its development [1][2][4]. Group 1: Development Timeline - The anticipation for R2 began after the release of the DeepSeek-V3 model in December last year, which was considered a benchmark for cost-performance [5]. - Initial expectations suggested that R2 would be launched in April, following the upgrade of V3 on March 24 [11]. - Despite the release of a paper on inference scaling in April, there has been no official update on R2's launch [12][16]. Group 2: Technical Specifications - R1's training utilized 30,000 H20 chips, 10,000 H800 chips, and 10,000 H100 chips, indicating the significant computational resources required for R2 [3]. - Leaked parameters for R2 suggested it would have 1.2 trillion parameters and utilize 5.2 petabytes of training data, raising questions about its hardware requirements [17]. Group 3: Community Reactions - Following the news of the delay, community responses varied, with some expressing belief that the delay would be worthwhile, while others speculated that R2 might wait for the release of V4 [26][28].

Group 1 - The event focused on building a digitally empowered learning society, with representatives sharing successful cases of digital education initiatives [1] - The National Open University aims to create a lifelong education network that connects urban and rural areas, serving as a foundation for a learning-oriented society [1] - The university's digital education platform has served 9.63 million learners in just six months, showcasing the effectiveness of digital technology in enhancing learning resources [1] Group 2 - Wuhan is leveraging its red education resources to create a "virtual three-dimensional digital memorial hall," expecting 7.26 million visitors in 2024 [2] - Changsha Civil Affairs Vocational College is digitizing practical teaching scenarios by developing virtual nursing homes and a digital museum of Chinese elderly culture, integrating 48 virtual simulation training systems [2] - Zhejiang Province is implementing a "five-level connection" framework to build the "Zhejiang Learning Pass" core platform, ensuring educational resources are accessible at the community level [2] Group 3 - The Ministry of Education plans to strengthen the national lifelong education platform to gather diverse learning resources and promote collaboration among school, family, and social education [2]