R-Zero is a self-evolving framework for Large Language Models (LLMs) that generates its own training data autonomously, circumventing reliance on human-curated tasks. It features two models—the Challenger, which poses increasingly difficult tasks, and the Solver, which solves them—allowing for co-evolution and significant improvements in reasoning capabilities across various benchmarks. Empirical results show notable enhancements in performance, particularly with the Qwen3-4B-Base model.