@inproceedings{eshuijs-etal-2026-safety,
title = "Safety Training Modulates Harmful Misalignment Under On-Policy {RL}, But Direction Depends on Environment Design",
author = "Eshuijs, Leon and
Wang, Shihan and
Fokkens, Antske",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2461/",
pages = "55879--55908",
ISBN = "979-8-89176-459-0",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Safety Training Modulates Harmful Misalignment Under On-Policy RL, But Direction Depends on Environment Design
%A Eshuijs, Leon
%A Wang, Shihan
%A Fokkens, Antske
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F eshuijs-etal-2026-safety
%X 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.
%U https://aclanthology.org/2026.emnlp-main.2461/
%P 55879-55908
Markdown (Informal)
[Safety Training Modulates Harmful Misalignment Under On-Policy RL, But Direction Depends on Environment Design](https://aclanthology.org/2026.emnlp-main.2461/) (Eshuijs et al., EMNLP 2026)
ACL