Forgetting That Sticks: Quantization-Permanent Unlearning via Circuit Attribution
Standard unlearning evaluations measure behavioral suppression in full precision, immediately after training, despite every deployed language model being quantized first. Recent work has shown that 4-bit post-training quantization can reverse machine unlearning; we show this is not a tuning artefact but a systematic dual failure: gradient-based methods that achieve meaningful forgetting lose it under compression, while methods that survive quantization barely change the model. Both failures trace to the same root cause: across all baselines, per-parameter updates lie 47-828x below the NF4 quantization bin width; updates diffused across billions of parameters cannot clear quantization bin ...
| Authors | Saisab Sadhu, Pratinav Seth, Vinay Kumar Sankarapu |
|---|---|
| Primary category | cs.LG |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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