近两百万人围观的Karpathy年终大语言模型清单，主角是它们

Core Insights - 2025 is a pivotal year for the evolution of large language models (LLMs), marked by significant paradigm shifts and advancements in the field [2][36] - The emergence of Reinforcement Learning from Verifiable Rewards (RLVR) is transforming LLM training processes, leading to enhanced capabilities without necessarily increasing model size [10][11] - The industry is witnessing a new layer of LLM applications, exemplified by tools like Cursor, which organize and deploy LLM capabilities in specific verticals [16][17] Group 1: Reinforcement Learning and Model Training - The introduction of RLVR allows models to learn in verifiable environments, enhancing their problem-solving strategies through self-optimization [10] - The majority of capability improvements in 2025 stem from extended RL training rather than increased model size, indicating a new scaling law [11][12] - OpenAI's models, such as o1 and o3, exemplify the practical application of RLVR, showcasing a significant qualitative leap in performance [12] Group 2: Understanding LLM Intelligence - The industry is beginning to grasp the unique nature of LLM intelligence, which differs fundamentally from human intelligence, leading to a jagged distribution of capabilities [14][15] - The concept of "vibe coding" emerges, allowing non-engineers to create complex programs, thus democratizing programming and reshaping software development roles [25][29] - The introduction of tools like Claude Code signifies a shift towards LLM agents that can operate locally, enhancing user interaction and productivity [19][22] Group 3: User Interaction and GUI Development - The development of GUI applications like Google Gemini's "Nano Banana" indicates a trend towards more intuitive and visually engaging interactions with LLMs [31][34] - The integration of text, images, and knowledge within a single model represents a significant advancement in how LLMs can communicate and operate [34] - The industry is at the cusp of a new interaction paradigm, moving beyond traditional web-based AI to more integrated and user-friendly applications [23][30] Group 4: Future Outlook - The potential of LLMs remains largely untapped, with the industry only beginning to explore their capabilities [38][39] - Continuous and rapid advancements are expected, alongside the recognition of the extensive work still required to fully realize the potential of LLM technology [40][41]