DynaMiCS: Fine-Tuning LLMs with Performance Constraints Using Dynamic Mixtures

Apple researchers have introduced DynaMiCS, a framework designed to improve the fine-tuning of large language models by dynamically adjusting data mixtures during training. By prioritizing specific performance constraints, this method helps models learn new domain-specific tasks without degrading their existing safety, general knowledge, or instruction-following capabilities.