Behavioural Analysis of Alignment Faking
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 activa...
| Authors | Nathaniel Mitrani Hadida, Rhea Karty, David Williams-King et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 May 2026 |
| Last updated at source | 3 August 2026 |
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