Self-Adapting Language Models
Adam Zweiger, Jyothish Pari, Han Guo, Yoon Kim, Pulkit Agrawal
Large language models (LLMs) are powerful but static; they lack mechanisms to adapt their weights in response to new tasks, knowledge, or examples. We introduce **Se**lf-**A**dapting **L**LMs (SEAL), a framework that enables LLMs to self-adapt by restructuring, filtering, and selectively generating training data for themselves. Given a new input, the model produces a *self-edit*, which may rewrite or restructure content directly, or invoke tools for data augmentation and finetuning. These self-edits drive targeted gradient updates, and a reinforcement-learning meta-optimization loop refines the policy that generates them, where the reward comes from the post-update model performance. SEAL leverages the model's autoregressive generation to take information and create weight-update directives that steer its own adaptation. Evaluations on knowledge incorporation and few-shot task learning indicate that SEAL is a promising step toward LLMs capable of autonomous adaptation.