Researchers proposed a self-training framework for large language models. It uses self-generated question-answer pairs and reasoning demonstrations. The model creates its own curriculum from high-level topic descriptions alone. It improves across text and multimodal tasks without human-curated prompts. Capabilities on untrained domains remain intact. The approach works on standard language and visual question-answering benchmarks. The paper shows foundation models can amplify latent abilities without external supervision.