生成式AI 一、 OpenAI强化微调终于上线，几十个样本可轻松打造AI专家 1. OpenAI正式发布RFT(强化微调)功能，通过思维链推理和专属评分机制，可用极少样本快 速提升模型在特定领域的专业表现； 2. RFT主要应用于三大场景：指令转代码、文本精华提取、复杂规则应用，已有ChipStack 等多家公司取得显著成效； 3. 实施RFT前必须创建评估体系，需要明确任务定义和强化评分方案，避免模棱两可的任务 目标。 https://mp.weixin.qq.com/s/c7RfeoWNwh3NZDeuTCXXLw 二、 Gemini 2.5实现视频理解重大突破：一口气处理6小时视频 1. Gemini 2.5 Pro突破视频处理长度限制，通过低媒体分辨率技术可处理长达6小时视频， 在多个学术基准测试中创下新纪录； 2. 实现视频内容与代码无缝结合，能将视频直接转化为交互式网页应用、p5.js动画等创新应 用形式； 3. 具备精准的视频片段检索和时序推理能力，可实现复杂场景计数、时间戳定位等高级分析 功能。 https://mp.weixin.qq.com/s/FkaOacVuVCS7wzny5l1jFQ ...

Core Viewpoint - Enigma Labs has developed the world's first AI-generated multiplayer game, Multiverse, which allows players to interact in a dynamically evolving world at a low development cost of under $1,500 [2][3]. Group 1: Game Development and Features - Multiverse is a multiplayer racing game where players can overtake, drift, and accelerate, reshaping the game world with each action [2][3]. - The game operates on a model that allows real-time interaction with an AI-simulated environment, filling a gap in AI-generated worlds [3][6]. - The development team plans to open-source all related research, including code, data, and architecture [3][8]. Group 2: Team Background - The team consists of former members of Israel's elite 8200 unit and experienced professionals from leading startups, specializing in research and engineering [5]. Group 3: Technical Architecture - The architecture of the multiplayer model builds upon existing single-player models, requiring a redesign of input and output connections to facilitate cooperative gameplay [12][14]. - The model integrates player actions and frame data to ensure a consistent shared world state, crucial for multiplayer interactions [14][15]. Group 4: Data Collection and Training - The training data for the model was sourced from Sony's Gran Turismo 4, with the team modifying the game to enable a 1v1 mode for data collection [39][41]. - The team utilized computer vision to extract control inputs from HUD elements displayed during gameplay, allowing for the reconstruction of player actions without direct input recording [46]. - A scalable method for data generation was implemented using the B-Spec mode, enabling automated race recordings from multiple perspectives [48]. Group 5: Model Training and Performance - The model was trained to predict future frames over varying time horizons, initially focusing on short-term predictions before extending to longer-term interactions [32][33]. - Efficient long-range training techniques were developed to manage memory constraints while maintaining performance [34][35].