一文读懂GPT-5的绝招，这是决定AI未来的隐形武器

Core Insights - The article discusses the significance of the "Universal Verifier" in the evolution of AI models, particularly in the context of GPT-5 and its performance enhancements [2][3] - It highlights the limitations of previous reinforcement learning methods, particularly "Reinforcement Learning with Verifiable Rewards" (RLVR), in complex real-world scenarios where answers are not binary [2][4] - The article outlines two main approaches to developing the Universal Verifier: enhancing the evaluation criteria and allowing models to self-assess their outputs [36][44] Group 1: Universal Verifier and Its Importance - The Universal Verifier is seen as a potential breakthrough in AI, addressing the shortcomings of RLVR by enabling models to evaluate answers in a more nuanced manner [2][10] - The need for a more sophisticated evaluation system arises from the complexity of real-world problems, especially in fields like healthcare and education, where answers are not simply right or wrong [2][11] - The article emphasizes that understanding the Universal Verifier is crucial for grasping the future of AI technology and competition [3] Group 2: Approaches to Developing the Universal Verifier - The first approach involves using large language models (LLMs) as judges to create a more complex evaluation standard, which has been explored in various research papers [4][5][6] - The second approach focuses on self-assessment, where models evaluate their own outputs based on internal confidence levels, reducing reliance on external validation [44][45] - The RaR (Rubrics as Rewards) framework is introduced as a method to create detailed scoring criteria for evaluating model outputs, leading to significant performance improvements in specific domains [19][21][22] Group 3: Performance Improvements and Results - The article presents data showing that models trained using the RaR framework achieved substantial performance gains, with scores in medical evaluations increasing nearly fourfold [21][22] - Comparisons with other evaluation methods indicate that RaR outperformed traditional approaches, demonstrating its effectiveness in complex reasoning tasks [22][24] - The Rubicon framework further enhances the scoring system by incorporating over 10,000 evaluation criteria, leading to improved performance in subjective areas like creative writing [27][28] Group 4: Future Directions and Challenges - The article discusses the limitations of current approaches, noting that while RaR and Rubicon show promise, they still rely on expert-defined criteria, which may hinder scalability [69][70] - The INTUITOR method represents a shift towards internal feedback mechanisms, allowing models to learn without predefined answers, but it also faces challenges in generalizability [59][60] - The OaK architecture is proposed as a long-term vision for AI, aiming for a system that learns and evolves through interaction with the environment, though it remains a distant goal [70][77]