Large language models (LLMs) typically cannot adapt their weights dynamically to new tasks or knowledge. The Self-Adapting LLMs (SEAL) framework addresses this limitation by allowing models to generate their own finetuning data and directives for self-adaptation through a reinforcement learning approach, resulting in persistent weight updates and improved performance in knowledge incorporation and few-shot generalization tasks.