Core Viewpoint - The article discusses the R-Zero framework, which enables AI models to self-evolve from "zero data" through a collaborative evolution of two AI roles: Challenger and Solver, aiming to overcome the limitations of traditional large language models that rely on extensive human-annotated data [2][3]. Group 1: R-Zero Framework Overview - R-Zero is designed to allow AI to self-generate learning tasks and improve reasoning capabilities without human intervention [11]. - The framework consists of two independent yet collaboratively functioning agents: Challenger (Qθ) and Solver (Sϕ) [6]. - The Challenger acts as a course generator, creating tasks that are at the edge of the Solver's current capabilities, focusing on tasks with high information gain [6]. Group 2: Iterative Process - The process involves an iterative loop where the Challenger trains on the frozen Solver model to generate questions that maximize the Solver's uncertainty [8]. - After each iteration, the enhanced Solver becomes the new target for the Challenger's training, leading to a spiral increase in both agents' capabilities [9]. Group 3: Implementation and Results - The framework generates pseudo-labels through a self-consistency strategy, where the Solver produces multiple candidate answers for each question, selecting the most frequent as the pseudo-label [17]. - A filtering mechanism ensures that only questions with a specific accuracy range are retained for training, enhancing the quality of the learning process [18]. - Experimental results show significant improvements in reasoning capabilities, with the Qwen3-8B-Base model's average score in mathematical benchmarks increasing from 49.18 to 54.69 after three iterations (+5.51) [18]. Group 4: Generalization and Efficiency - The model demonstrates strong generalization capabilities, with average scores in general reasoning benchmarks like MMLU-Pro and SuperGPQA improving by 3.81 points, indicating enhanced core reasoning abilities rather than mere memorization of specific knowledge [19]. - The R-Zero framework can serve as an efficient intermediate training stage, maximizing the value of human-annotated data when used for subsequent fine-tuning [22]. Group 5: Challenges and Limitations - A key challenge identified is the decline in the accuracy of pseudo-labels, which dropped from 79.0% in the first iteration to 63.0% in the third, indicating increased noise in the supervisory signals as task difficulty rises [26]. - The framework's reliance on domains with objective, verifiable answers limits its applicability in areas with subjective evaluation criteria, such as creative writing [26].