This recent MIT paper details a novel framework called Self-Adapting Language Models (SEAL), which allows large language models (LLMs) to modify their own weights through self-generated training data and update instructions, termed self-edits. The core mechanism involves an outer reinforcement learning (RL) loop that trains the model to create effective self-edits, rewarding generations that improve performance on downstream tasks, and an inner supervised finetuning (SFT) loop that applies the resulting weight updates. Experiments demonstrate SEAL's effectiveness in two key domains: knowledge incorporation of new factual information and few-shot generalization by autonomously selecting data augmentations and optimization hyperparameters. Overall, SEAL offers a versatile approach for enabling LLMs to overcome their static nature and engage in self-directed adaptation in response to new inputs and tasks.