Self-Adapting Language Models
Description
This paper introduces Self-Adapting Large Language Models (SEAL), a novel framework that enables LLMs to autonomously improve by generating their own training data and finetuning instructions, termed "self-edits." This adaptation process is driven by a reinforcement learning (RL) loop that rewards the model for generating self-edits that subsequently improve its performance on downstream tasks, contrasting with static models that learn from data "as-is." The authors demonstrate SEAL's effectiveness in two key domains: knowledge incorporation, where it generates synthetic data to efficiently integrate new facts, and few-shot learning, where it autonomously configures optimal data augmentations and training hyperparameters. Although promising, the work notes limitations regarding computational overhead and susceptibility to catastrophic forgetting during continuous adaptation.
United States
