Compatibility-Aware Dynamic Fine-Tuning for Large Language Models 事件

Compatibility-Aware Dynamic Fine-Tuning for Large Language Models arXiv:2606.11206v1 Announce Type: new Abstract: Supervised Fine-Tuning (SFT) is the predominant paradigm for aligning large language models (LLMs), yet it suffers from optimization instability and limited generalization. Recent work attributes this issue to pathological gradient scaling and proposes Dynamic Fine-Tuning (DFT) to correct it at the token level. However, DFT assumes all demonstrations are equally suitable learning ta