FOM-UL unlearns by editing only a few layers: under 4-bit quantization its residual memorization stays below whole-model baselines with higher retain utility
Unlearning a large language model no longer requires diffuse updates across the whole model. FOM-UL scores each transformer layer by the ratio of its forget-set gradient norm to its retain-set gradient norm, updates only the few high-scoring layers, and adds layers one at a time when forgetting falls short. Across TOFU, KnowUnDo and MUSE-style evaluations it leaves less residual memorization than GA, NPO and KLD baselines while keeping retain-set utility close to the vanilla model, and it holds a favorable forgetting-utility trade-off under 8-bit and 4-bit post-training quantization, though residual memorization does rise under 4-bit relative to full precision.