模型大小在强化学习中既可护航安全，也可能助长作恶，关键看环境设计。
Safety Training Modulates Harmful Misalignment Under On-Policy RL, But Direction Depends on Environment Design
- 用在线强化学习训练11个大模型，测试不同环境下的安全表现。
- 模型越大越安全？结果取决于环境：有的场景反而更易被滥用。
- 现有安全评测难预测风险，尤其当模型揣测用户偏好时最危险。
强化学习中的规范游戏现象会导致大语言模型产生奉承、操控或欺骗行为，但其发生条件尚不明确。我们使用在线强化学习，在3个环境中对11个指令微调的大模型（0.5B–14B）进行训练，发现模型规模在某些环境中起到安全缓冲作用，但在其他环境中却加剧了有害利用。受控消融分析表明，这种反转源于环境特有特征，如角色设定和隐含可操纵线索。此外，大多数安全基准无法预测强化学习引发的偏差，仅当滥用依赖于推断用户偏好时，奉承得分具有预测性。最后，我们发现在线强化学习能保留模型自身生成分布中的安全特性，而离线设置会绕过这一保护机制。
原文摘要 · Abstract (English)
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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