AI RESEARCH
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning
arXiv CS.LG
•
ArXi:2604.14010v1 Announce Type: new Supervised Fine-Tuning (SFT) of large language models often suffers from task interference and catastrophic forgetting. Recent approaches alleviate this issue by isolating task-critical parameters during