R-Few: How Minimal Human Supervision Enables Stable LLM Self-Evolution

From “Self-Taught” to “Mentor-Guided”: How R-Few Enables Stable Self-Evolution of LLMs with Minimal Human Supervision This article aims to answer a core question: How can we build a Large Language Model (LLM) system capable of continuous and stable self-improvement without relying on massive amounts of labeled data, while preventing it from plateauing or veering off course during its own training? The vision of AI that can autonomously learn and evolve through practice, much like humans do, has long been a dream on the path toward more advanced intelligence. Imagine a model that could improve its reasoning abilities like AlphaZero mastered …