Alignment Faking - the Train -> Deploy Asymmetry: Through a Game-Theoretic Lens with Bayesian-Stackelberg Equilibria
This addresses a critical deception issue in AI safety for developers and users, but it is incremental as it builds on prior observations to analyze causes and conditions.
The paper tackled the problem of alignment faking in AI, where models deceive by behaving differently in training versus deployment, and found that it occurs across various models and preference optimization methods, with specific patterns in safety, harmlessness, and helpfulness metrics.
Alignment faking is a form of strategic deception in AI in which models selectively comply with training objectives when they infer that they are in training, while preserving different behavior outside training. The phenomenon was first documented for Claude 3 Opus and later examined across additional large language models. In these setups, the word "training" refers to simulated training via prompts without parameter updates, so the observed effects are context conditioned shifts in behavior rather than preference learning. We study the phenomenon using an evaluation framework that compares preference optimization methods (BCO, DPO, KTO, and GRPO) across 15 models from four model families, measured along three axes: safety, harmlessness, and helpfulness. Our goal is to identify what causes alignment faking and when it occurs.