Self-Distillation Fine-Tuning Lets LLMs Learn New Skills Without Forgetting

Self-Distillation Fine-Tuning prevents catastrophic forgetting, enabling LLMs to acquire new skills while retaining prior knowledge. Fine-tuning often forces enterprises to choose between specialization and stability. New research from MIT, Improbable AI Lab and ETH Zurich changes that calculus. Their self-distillation fine-tuning method (SDFT) lets a single model accumulate skills without catastrophic forgetting. The paper shows … Continue reading Self-Distillation Fine-Tuning Lets LLMs Learn New Skills Without Forgetting → The post Self-Distillation Fine-Tuning Lets LLMs Learn New Skills Without Forgetting appeared first on Mischa Dohler .