# Self-Adapting Language Models #paper https://arxiv.org/html/2506.10943v1 This paper introduces Self-Adapting Language Models (SEAL), a framework that enables large language models (LLMs) to adapt themselves by generating their own synthetic training data and finetuning directives. The authors—Jyo Pari, Shivam Duggal, Idan Shenfeld, Seungwook Han, Jeremy Bernstein, Akarsh Kumar, Linlu Qiu, Juno Kim, Brian Cheung, Moritz Reuss, Ayush Sekhari, Zhang-Wei Hong, Mehul Damani, Leshem Choshen, and Ryan Yang—propose that LLMs can improve their performance on new tasks and incorporate new knowledge by creating and learning from their own self-edits. SEAL uses a reinforcement learning loop, where the model generates self-edits (such as restructured information, optimization hyperparameters, or data augmentation instructions) and is rewarded based on improved downstream performance. This approach differs from traditional methods that rely on static data or external adaptation modules. Instead, SEAL allows the model to control its own adaptation process directly. The framework is tested in two main areas: knowledge incorporation (adding new factual information to the model) and few-shot learning (generalizing from a small number of examples). In both cases, SEAL outperforms standard baselines and even synthetic data generated by larger models like GPT-4.1. The results show improved accuracy in question answering and better adaptation to new tasks. The authors also discuss limitations such as catastrophic forgetting (where new learning can overwrite old knowledge), computational overhead, and the need for explicit downstream tasks for evaluation. They suggest that future work could address these challenges and extend SEAL to continual learning and agentic systems. In summary, SEAL represents a step towards more autonomous and adaptable language models, capable of self-directed learning and improvement. This could be especially important as the availability of human-generated training data becomes limited, making synthetic self-generated data increasingly valuable for future model development. For more details, see [[Self-Adapting Language Models|the full research paper on SEAL and its implications for future AI development]].