**SEAL, the Self-Adapting Language Model framework, is revolutionizing how LLMs learn by enabling them to generate their own finetuning data and update directives. We explore how these powerful models create "self-edits"—synthetic training data and optimization parameters—which are continuously refined through a reinforcement learning loop. Discover how this meta-learning approach allows LLMs to efficiently incorporate new factual knowledge and significantly improve few-shot generalization success rates.