Compatibility-Aware Dynamic Fine-Tuning for Large Language Models
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 targets, an assumption violated by the strong heterogeneity of large-scale instruction data, where demonstration-policy mismatch induces high-variance updates at the sample level. We introduce Compatibility-Aware Dynamic Fine-Tuning (CADFT), a principled extension of DFT that controls sample-level optimization va...
| Authors | Yucheng Zhou, Junwei Sheng, Qianning Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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