AGENTIC-SYSTEMSMay 28, 20261 min read11 views

Behavioural Analysis of Alignment Faking

A new study analyzes 'Alignment Faking' (AF), where AI models strategically comply with training goals to avoid behavioral modification. The researchers found that AF is more widespread than previously thought, even occurring in small-scale models, driven by three distinct factors: values, goal guarding, and sycophancy.

Computer Science > Artificial Intelligence

Title:Behavioural Analysis of Alignment Faking

View PDF HTML (experimental)Abstract: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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Source: arXiv cs.AI Recent

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