The provided text describes Self-Adapting Language Models (SEAL), a novel framework enabling large language models (LLMs) to learn and improve autonomously. Unlike static models, SEAL empowers LLMs to generate their own finetuning data and update instructions (termed "self-edits") in response to new information or tasks. This adaptation process is driven by a reinforcement learning loop, where the model is rewarded based on the performance of its self-updated version on downstream tasks. Experiments demonstrate SEAL's effectiveness in knowledge incorporation and few-shot generalization, showcasing its ability to optimize data transformation and training parameters, even outperforming synthetic data generated by larger, more advanced models like GPT-4.1. The research highlights a significant step towards LLMs that can continuously learn and refine their own capabilities.