The academic paper presents the Self-Adapting LLM (SEAL) framework, designed to allow large language models to overcome their static nature by transforming and generating their own fine-tuning data. This mechanism involves the model producing a "self-edit," which consists of natural-language instructions that specify synthetic data, tool invocations, or optimization hyperparameters for adaptation. Training is managed by an outer reinforcement learning (RL) loop that rewards the model based on the improved performance achieved after the self-edit results in persistent weight updates via supervised fine-tuning. Evaluations show that SEAL significantly enhances both knowledge incorporation of new factual data and few-shot generalization on abstract reasoning tasks. Ultimately, the authors propose this work as a viable strategy for enabling models to pursue self-directed, continual learning in preparation for a future where traditional human-generated data sources are exhausted.

Source:

https://openreview.net/pdf?id=JsNUE84Hxi