GRP-Obliteration: Unaligning LLMs with a Single Unlabeled Prompt
Despite extensive work on safety post-training, it has been shown that models can be readily unaligned through post-deployment fine-tuning. However, these methods often require extensive data curation and degrade model utility. In this work, we extend the practical limits of unalignment by introducing GRP-Obliteration (GRP-Oblit), a method that uses Group Relative Policy Optimization (GRPO) to directly remove safety constraints from target models.
- ▪Despite extensive work on safety post-training, it has been shown that models can be readily unaligned through post-deployment fine-tuning.
- ▪However, these methods often require extensive data curation and degrade model utility.
- ▪In this work, we extend the practical limits of unalignment by introducing GRP-Obliteration (GRP-Oblit), a method that uses Group Relative Policy Optimization (GRPO) to directly remove safety constraints from target models.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2602.06258 |
| Publication time | Tue, 15 Sep 2026 14:31:23 +0000 |
| Retrieval time | 2026-09-15T17:16:52.647Z |
| Last seen | 2026-09-15T17:16:52.647Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Machine Learning arXiv:2602.06258 (cs) [Submitted on 5 Feb 2026] Title:GRP-Obliteration: Unaligning LLMs With a Single Unlabeled Prompt Authors:Mark Russinovich, Yanan Cai, Keegan Hines, Giorgio Severi, Blake Bullwinkel, Ahmed Salem View a PDF of the paper titled GRP-Obliteration: Unaligning LLMs With a Single Unlabeled Prompt, by Mark Russinovich and 5 other authors View PDF HTML (experimental) Abstract:Safety alignment is only as robust as its weakest failure mode. Despite extensive work on safety post-training, it has been shown that models can be readily unaligned through post-deployment fine-tuning. However, these methods often require extensive data curation and degrade model utility.
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