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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.12500 |
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| _version_ | 1866915936475283456 |
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| author | Eshuijs, Leon Wang, Shihan Fokkens, Antske |
| author_facet | Eshuijs, Leon Wang, Shihan Fokkens, Antske |
| contents | Specification gaming under Reinforcement Learning (RL) is known to cause LLMs to develop sycophantic, manipulative, or deceptive behavior, yet the conditions under which this occurs remain unclear. We train 11 instruction-tuned LLMs (0.5B--14B) with on-policy RL across 3 environments and find that model size acts as a safety buffer in some environments but enables greater harmful exploitation in others. Controlled ablations trace this reversal to environment-specific features such as role framing and implicit gameability cues. We further show that most safety benchmarks do not predict RL-induced misalignment, except in the case of Sycophancy scores when the exploit relies on inferring the user's preference. Finally, we find that on-policy RL preserves a safety buffer inherent in the model's own generation distribution, one that is bypassed during off-policy settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12500 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Safety Training Modulates Harmful Misalignment Under On-Policy RL, But Direction Depends on Environment Design Eshuijs, Leon Wang, Shihan Fokkens, Antske Machine Learning Cryptography and Security Specification gaming under Reinforcement Learning (RL) is known to cause LLMs to develop sycophantic, manipulative, or deceptive behavior, yet the conditions under which this occurs remain unclear. We train 11 instruction-tuned LLMs (0.5B--14B) with on-policy RL across 3 environments and find that model size acts as a safety buffer in some environments but enables greater harmful exploitation in others. Controlled ablations trace this reversal to environment-specific features such as role framing and implicit gameability cues. We further show that most safety benchmarks do not predict RL-induced misalignment, except in the case of Sycophancy scores when the exploit relies on inferring the user's preference. Finally, we find that on-policy RL preserves a safety buffer inherent in the model's own generation distribution, one that is bypassed during off-policy settings. |
| title | Safety Training Modulates Harmful Misalignment Under On-Policy RL, But Direction Depends on Environment Design |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2604.12500 |