Dynamic Adversarial Fine-Tuning Reorganizes Refusal Geometry
Safety-aligned language models must refuse harmful requests without broad over-refusal, but it remains unclear how dynamic adversarial fine-tuning changes refusal-control carriers: Kullback--Leibler (KL)-constrained directions or small subspaces that causally modulate refusal without large safe-prompt distribution shifts. We study a 7B backbone under supervised fine-tuning (SFT) and Robust Refusal Dynamic Defense (R2D2), aligning HarmBench, StrongREJECT, and XSTest evaluations with five-anchor geometry measurements, causal interventions, and sparse adaptive stress tests. R2D2 drives fixed-source HarmBench attack success to zero at early checkpoints; however, these checkpoints also exhibit...
| Authors | Wenhao Lan, Shan Li, Xinhua Lai et al. |
|---|---|
| Primary category | cs.LG |
| Published | 29 April 2026 |
| Last updated at source | 25 May 2026 |
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