MIT's new fine-tuning method lets LLMs learn new skills without losing old ones

MIT's new fine-tuning method lets LLMs learn new skills without losing old ones

When enterprises fine-tune LLMs for new tasks, they risk breaking everything the models already know. This forces companies to maintain separate models for every skill.Researchers at MIT, the Improbable AI Lab and ETH Zurich have developed a new technique that enables large language models to learn new skills and knowledge without forgetting their past capabilities.Their technique, called self-distillation fine-tuning (SDFT), allows models to le…

Researchers propose a self-distillation fix for ‘catastrophic forgetting’ in LLMs

A new fine-tuning technique aims to solve "catastrophic forgetting," a limitation that often complicates repeated model updates in enterprise deployments. Researchers at MIT, the Improbable AI Lab, and ETH Zurich have introduced a fine-tuning method designed to let models learn new tasks while preserving previously acquired capabilities. To prevent degrading existing capabilities, many organizations isolate new tasks into separate fine-tuned mod…