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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.05024 |
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| _version_ | 1866917047408001024 |
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| author | Wichers, Nevan Ebtekar, Aram Azarbal, Ariana Gillioz, Victor Ye, Christine Ryd, Emil Rathi, Neil Sleight, Henry Mallen, Alex Roger, Fabien Marks, Samuel |
| author_facet | Wichers, Nevan Ebtekar, Aram Azarbal, Ariana Gillioz, Victor Ye, Christine Ryd, Emil Rathi, Neil Sleight, Henry Mallen, Alex Roger, Fabien Marks, Samuel |
| contents | Large language models are sometimes trained with imperfect oversight signals, leading to undesired behaviors such as reward hacking and sycophancy. Improving oversight quality can be expensive or infeasible, motivating methods that improve learned behavior despite an imperfect training signal. We introduce Inoculation Prompting (IP), a simple but counterintuitive technique that prevents learning of an undesired behavior by modifying training prompts to explicitly request it. For example, to inoculate against reward hacking, we modify the prompts used in supervised fine-tuning to request code that only works on provided test cases but fails on other inputs. Across four settings we find that IP reduces the learning of undesired behavior without substantially reducing the learning of desired capabilities. We also show that prompts which more strongly elicit the undesired behavior prior to fine-tuning more effectively inoculate against the behavior when used during training; this serves as a heuristic to identify promising inoculation prompts. Overall, IP is a simple yet effective way to control how models generalize from fine-tuning, preventing learning of undesired behaviors without substantially disrupting desired capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05024 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inoculation Prompting: Instructing LLMs to misbehave at train-time improves test-time alignment Wichers, Nevan Ebtekar, Aram Azarbal, Ariana Gillioz, Victor Ye, Christine Ryd, Emil Rathi, Neil Sleight, Henry Mallen, Alex Roger, Fabien Marks, Samuel Machine Learning Large language models are sometimes trained with imperfect oversight signals, leading to undesired behaviors such as reward hacking and sycophancy. Improving oversight quality can be expensive or infeasible, motivating methods that improve learned behavior despite an imperfect training signal. We introduce Inoculation Prompting (IP), a simple but counterintuitive technique that prevents learning of an undesired behavior by modifying training prompts to explicitly request it. For example, to inoculate against reward hacking, we modify the prompts used in supervised fine-tuning to request code that only works on provided test cases but fails on other inputs. Across four settings we find that IP reduces the learning of undesired behavior without substantially reducing the learning of desired capabilities. We also show that prompts which more strongly elicit the undesired behavior prior to fine-tuning more effectively inoculate against the behavior when used during training; this serves as a heuristic to identify promising inoculation prompts. Overall, IP is a simple yet effective way to control how models generalize from fine-tuning, preventing learning of undesired behaviors without substantially disrupting desired capabilities. |
| title | Inoculation Prompting: Instructing LLMs to misbehave at train-time improves test-time alignment |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.05024 |