对齐伪装中的训练-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 对齐伪装中的"训练-部署不对称"现象，提出贝叶斯-Stackelberg 均衡模型。对齐伪装指模型推断自己处于训练时选择性服从训练目标、在训练外保留不同行为，该现象最早在 Claude 3 Opus 上记录，后在更多大语言模型中被考察。

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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