作者 | 水中刀 12 月 30 日 Meta 正式宣布与 Manus 达成全资收购协议，收购金额高达数十亿美元，成为 Meta 成 立以来，仅次于 WhatsApp（190 亿美元）和 Scale AI（未披露具体金额）的第三大收购案。 Manus 创始人肖弘将出任 Meta 副总裁，其核心技术团队将整体并入 Meta AI 部门，公司将继续在 新加坡独立运营，现有订阅服务保持不变。肖弘在社交媒体上发文感慨： "今天是我一生都难以忘却的时刻。" 无论从产品还是营销层面来看，Manus 都堪称生成式 AI 时代的典型创业案例，但也有业内专家表 示，看来做一家独立公司很难，通用级 Agent 终究还是巨头的游戏。 这样的评价，也源于 Agent 想要落地企业，本身就存在许多工程交付与产品优化问题，而这一般来 说是"大厂专利"。就 ToB 场景而言，企业级 Agent 的使用频率，与资本、品牌市场层面的"表象繁 荣"是有些许错位的，创业公司很难组建庞大的工程师团队围绕细分场景做长线研发，遑论贴身服务 客户，在价值上形成闭环。 这些所谓的工程交付与产品优化问题，大概可分为以下几类： 幻觉：模型带来的幻觉问题，或许无 ...

Core Insights - The article discusses the emergence of Agentic RAG (Retrieval-Augmented Generation) as a key method for large language models to access external knowledge, highlighting the limitations of current reinforcement learning (RL) training methods in achieving stable performance [1][8]. Group 1: Development of RAG Systems - The evolution of RAG systems is categorized into three stages: Classic RAG, Pre-RL-Zero Active RAG, and RL-Zero stage, with each stage introducing new methodologies to enhance retrieval and generation capabilities [7][8]. - The RL-based methods, while promising, face challenges such as misalignment of optimization goals with actual downstream tasks and the coupling of retrieval and generation processes, which complicates performance evaluation [9][12]. Group 2: Limitations of Current RL Methods - Current RL methods like Search-R1 and DeepRetrieval focus on Exact Match (EM) as a reward metric, which can lead to suboptimal training outcomes due to its strictness and insensitivity to semantic variations [9][10]. - The coupling of retrieval and generation in training can obscure the true performance improvements, making it difficult to discern whether gains are due to better search or enhanced language generation [11][12]. - Existing evaluation metrics fail to accurately measure the contribution of search quality to overall performance, leading to bottlenecks in assessment, training, and generalization [14]. Group 3: Introduction of s3 Framework - The s3 framework, proposed by UIUC and Amazon, aims to improve training efficiency and effectiveness by decoupling the search and generation processes, focusing solely on optimizing the searcher with a new reward function called Gain Beyond RAG (GBR) [1][17]. - s3 demonstrates significant efficiency, requiring only 2.4k training samples and achieving superior performance compared to larger baseline models, with a total training time of just 114 minutes [21][22][25]. Group 4: Experimental Results - In general QA tasks, s3 outperformed both Search-R1 and DeepRetrieval across multiple datasets, showcasing its strong generalization capabilities [23][25]. - In medical QA tasks, s3 exhibited remarkable cross-domain performance, indicating its robustness and adaptability to different datasets and contexts [26][27]. Group 5: Design and Optimization Insights - The design of s3 emphasizes the importance of starting retrieval from the original query, which helps maintain focus and improves search outcomes [31]. - The document selection mechanism within s3 significantly reduces token consumption, enhancing efficiency and minimizing noise in the generation process [31][30].