Xiaoyu Guan

DPI: EXPLOITING PARAMETER HETEROGENEITY FOR INTERFERENCE-FREE FINE-TUNING

Supervised fine-tuning (SFT) is a crucial step for adapting large language models (LLMs) to downstream tasks. However, conflicting objectives across heterogeneous SFT tasks often induce the "seesaw effect": optimizing for one task may degrade performance on others, particularly when model parameters…