JAN 07, 2026 2026 年 1 ⽉ 7 ⽇ ∙ PAID ∙ 付费内容 79 Share 分享 RL Environments and RL for Science: Data Foundries and Multi-Agent Architectures 强化学习环境与科学强化学习：数据⼯⼚与多智能 体架构 Worker Automation, RL as a Service, Anthropic's next big bet, GDPval and Utility Evals, Computer Use Agents, LLMs in Biology, Mid-Training, Lab Procurement Patterns, Platform Politics and Access Last June, we argued that scaling RL is the critical path to unlocking further AI capabilities. As we will show, the past several months have affirmed our ...

Model Performance & Innovation - Runway's latest V2 model, Runway Ten 4.5%, tops performance charts across all other models [2] - The company is the first to lead leaderboards, surpassing large research labs with consistent, realistic, and creative results [2][4] - Focus, research, and efficiency enable the company to compete with larger research labs [4] - The company has been developing models for almost seven years, building intuition and momentum for improvement [5] - Algorithmic improvements, data captioning, structuring, and model testing are key to pre-training [7] Business Model & Monetization - The company raised $300 million in April with a $3.3 billion valuation [6] - The company utilizes subscriptions and credits for model usage [10] - The model is being released to gaming companies, studios, brands, production companies, and creatives worldwide, with tens of millions of users [10] - The company makes money every time the model is used, with good margins [11][12] - The model is cost-effective compared to other models while maintaining top performance [13]

Actually if you want to know what the real ‘secret’ is 😀 it’s world-class research AND world-class engineering AND world-class infra all working closely together with relentless focus and intensity…Oriol Vinyals (@OriolVinyalsML):The secret behind Gemini 3?Simple: Improving pre-training & post-training 🤯Pre-training: Contra the popular belief that scaling is over—which we discussed in our NeurIPS '25 talk with @ilyasut and @quocleix—the team delivered a drastic jump. The delta between 2.5 and 3.0 is https:/ ...

Model Reasoning and Applications - Reasoning unlocks new language model applications, exemplified by improved information retrieval [1] - Reasoning models are enhancing applications like website analysis and code assistance, making them more steerable and user-friendly [1] - Reasoning models are pushing the limits of task completion, requiring ongoing effort to determine what models need to continue progress [1] Planning and Training - Planning is a new frontier for language models, requiring a shift in training approaches beyond just reasoning skills [1][2] - The industry needs to develop research plans to train reasoning models that can work autonomously and have meaningful planning capabilities [1] - Calibration is crucial for products, as models tend to overthink, requiring better management of output tokens relative to problem difficulty [1] - Strategy and abstraction are key subsets of planning, enabling models to choose how to break down problems and utilize tools effectively [1] Reinforcement Learning and Compute - Reinforcement learning with verifiable rewards is a core technique, where language models generate completions and receive feedback to update weights [2] - Parallel compute enhances model robustness and exploration, but doesn't solve every problem, indicating a need for balanced approaches [3] - The industry is moving towards considering post-training as a significant portion of compute, potentially reaching parity with pre-training in GPU hours [3]

Core Insights - The article discusses the evolution of AI, particularly focusing on the "trinity" of pre-training, post-training, and reasoning, and how these components are essential for achieving Artificial General Intelligence (AGI) [3][4][5] - Bob McGrew emphasizes that reasoning will be a significant focus in 2025, with many opportunities for optimization in compute usage, data utilization, and algorithm efficiency [4][5][6] - The article highlights the diminishing returns of pre-training, suggesting that while it remains important, its role is shifting towards architectural improvements rather than sheer computational power [6][8][9] Pre-training, Post-training, and Reasoning - Pre-training has reached a stage of diminishing returns, requiring exponentially more compute for marginal gains in intelligence [7][8] - Post-training focuses on enhancing the model's personality and intelligence, which can yield broad applicability across various fields [9][10] - Reasoning is seen as the "missing piece" that allows models to perform complex tasks through step-by-step thinking, which was previously lacking in models like GPT-3 [14][15] Agent Economics - The cost of AI agents is expected to approach the opportunity cost of compute usage, making it challenging for startups to maintain high pricing due to increased competition [17][18][19] - The article suggests that while AI can automate simple tasks, complex services requiring human understanding will retain their value and scarcity [19][20] Market Opportunities in Robotics - There is a growing interest in robotics, with the belief that the field is nearing commercialization due to advancements in language interfaces and visual encoding [22][25] - Companies like Skilled and Physical Intelligence are highlighted as potential leaders in the robotics space, capitalizing on existing technology and research [22][25] Proprietary Data and Its Value - Proprietary data is becoming less valuable compared to the capabilities of advanced AI models, which can replicate insights without extensive human labor [29][30] - The article discusses the importance of specific customer data that can enhance decision-making, emphasizing the need for trust in data usage [31] Programming and AI Integration - The integration of AI in programming is evolving, with a hybrid model where users engage in traditional coding while AI assists in the background [32][33] - The article notes that while AI can handle repetitive tasks, complex programming still requires human oversight and understanding [33][34] Future of AI and Human Interaction - The article explores how different generations interact with AI, suggesting that AI should empower individuals to become experts in their interests while alleviating mundane tasks [39][42] - It emphasizes the importance of fostering curiosity and problem-solving skills in the next generation, rather than merely teaching specific skills that may soon be automated [43][44]