Large language models (LLMs) perform well on general tasks but struggle with specialized work that requires understanding proprietary data, internal processes, and industry-specific terminology. Supervised fine-tuning (SFT) adapts LLMs to these organizational contexts. SFT can be implemented through two distinct methodologies: Parameter-Efficient Fine-Tuning (PEFT), which updates only a subset of model parameters, offering faster training and lower computational costs while maintaining reasonable performance improvements; Full-rank SFT, which updates all model parameters rather than a subset and incorporates more domain knowledge than PEFT.
Full-rank SFT often faces a challenge: catastrophic forgetting. As models learn domain-specific patterns, they lose general capabilities including instruction-following, reasoning, and broad knowledge. Organizations must choose between domain expertise and general intelligence, which limits model utility across enterprise use…