RapidUn reweights LoRA unlearning by cross-sample influence: lower seen- and OOD-trigger ASR than Fisher, GA, and LoReUn on Llama-3-8B, with a 77x speedup over clean-corpus retraining
The work proposes RapidUn, an influence-guided framework that converts cross-sample influence estimates into fixed sample-specific weights for weighted LoRA unlearning; across Llama-3-8B on Dolly-15k and Alpaca-57k, with cross-model validation on Mistral-7B + Dolly-15k, it achieves lower seen-trigger and OOD-trigger-family ASR than Fisher, GA, and LoReUn while maintaining competitive clean utility, attains a 77x wall-clock speedup over the clean-corpus LoRA retraining reference on Llama-3-8B + Alpaca-57k, and is further supported by TOFU, semantic LLM-judge, and IFEval evaluations.