This paper introduces Self-Adapting LLMs (SEAL), a novel framework enabling large language models to autonomously enhance their capabilities by generating and utilizing their own training data. Unlike static models, SEAL employs a reinforcement learning loop where the model produces "self-edits"—instructions for finetuning data and optimization—and is rewarded based on the performance of the updated model. Experiments demonstrate SEAL's effectiveness in knowledge incorporation and few-shot generalization, outperforming baselines and even synthetic data generated by larger models like GPT-4.1. The research also acknowledges limitations such as catastrophic forgetting and computational overhead, while envisioning future applications in continual learning and agentic systems.

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