Self-Adapting Language Models (SEAL) is a framework for enabling self-adaptation in large language models (LLMs).
SEAL allows LLMs to generate their own finetuning data and directives for adaptation.
The model's self-directed adaptation process involves producing self-edits that restructure information and optimize hyperparameters.
Adaptations are made persistent through supervised finetuning with reinforcement learning based on performance rewards.
SEAL does not require separate adaptation modules, making it distinct from previous methods.
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