Executable Self‑Curriculum for LLMs

LLMs can bootstrap their own improvement by generating solvable problems, executing candidate solutions in an environment (e.g., running code), and using pass/fail signals to fine‑tune themselves—producing high‑quality, scalable training data without human labeling. Early experiments (AZR on Qwen 7B/14B) show performance gains that can rival human‑curated corpora, though applicability is limited to verifiable task classes today. — If generalized beyond coding to agentic tasks, this technique could dramatically accelerate capability growth, decentralize who can train powerful models, and raise urgent governance questions about automated self‑improvement paths to high‑risk AI.