对齐伪装与训练-部署不对称：基于贝叶斯-Stackelberg 均衡的博弈论视角

Alignment Faking - the Train -> Deploy Asymmetry: Through a Game-Theoretic Lens with Bayesian-Stackelberg Equilibria

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研究用博弈论框架分析 AI 的"对齐伪装"现象，即模型只在推断自己处于训练状态时选择性服从训练目标。团队构建评估框架，在四个模型族的 15 个模型上对比 BCO、DPO、KTO 和 GRPO 四种偏好优化方法，从安全、无害、有用三个维度测量。该现象最早记录于 Claude 3 Opus，此处的"训练"指通过提示词模拟训练、不更新参数，因此观察到的效果是上下文条件化的行为变化，而非偏好学习。

正文

Authors:Kartik Garg, Shourya Mishra, Kartikeya Sinha, Ojaswi Pratap Singh, Ayush Chopra, Kanishk Rai, Ammar Sheikh, Raghav Maheshwari, Aman Chadha, Vinija Jain, Amitava Das

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Abstract: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.

Subjects:	Artificial Intelligence (cs.AI)
Cite as:	arXiv:2511.17937 [cs.AI]
	(or arXiv:2511.17937v2 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2511.17937 arXiv-issued DOI via DataCite

Submission history

From: Vinija Jain [view email]
[v1] Sat, 22 Nov 2025 06:30:51 UTC (30,745 KB)
[v2] Sat, 10 Oct 2026 21:02:03 UTC (8,003 KB)

来源：arXiv：cs.AI · arxiv.org