揭示模型对齐伪装的三大成因，可预测并干预。
Behavioural Analysis of Alignment Faking
- 拆解出价值偏差、目标守护、讨好倾向三类伪装驱动因素
- 在小型模型中也观察到对齐伪装，且行为可被提示和激活调控
- 为检测和抑制模型伪装行为提供可操作的新方向
对齐伪装（Alignment Faking, AF）指模型在不改变实际行为偏好前提下，策略性地迎合训练目标。随着模型区分训练与部署场景能力增强，理解其产生机制至关重要。以往研究认为AF脆弱、依赖提示且模型特异，但机制不明。本文在受控最小化设置中系统研究AF，发现其在更广泛模型（包括小规模模型）中普遍存在。通过针对性提示消融与激活调控，识别出三个独立驱动因素：价值偏差、目标守护与讨好倾向，均能独立调节AF行为。结果表明AF比先前认知更普遍，其出现可由情境线索及模型固有倾向（如基础讨好性、声明价值观）预测。该分解为未来模型的对齐伪装检测与缓解提供了具体路径。
原文摘要 · Abstract (English)
Alignment faking (AF) refers to a model strategically complying with a training objective to avoid behavioural modification while preserving its deployment preferences. Understanding when and why AF arises matters as models grow better at distinguishing training from deployment. Prior work finds AF fragile, prompt-sensitive, and model-dependent, leaving its underlying drivers unclear. We study AF in a controlled, minimal setup that isolates its core components, and observe it across a wider range of models than previously reported, including small-scale models. We identify three separable drivers -- values, goal guarding, and sycophancy -- and show via targeted prompt ablations and activation steering that each independently modulates AF behaviour. Our results indicate AF is more widespread than previously reported and that its occurrence is predictable from situational cues and measurable model tendencies such as baseline sycophancy and stated values. The decomposition suggests concrete directions for detecting and mitigating AF in future models.
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