NeurIPS 2025

Why Do Some Language Models Fake Alignment
While Others Don’t?
Abhay Sheshadri∗†
Anthropic
John Hughes∗
Anthropic
Julian Michael
Scale AI
Alex Mallen
Redwood Research
Arun Jose
Independent
Janus
Independent
Fabien Roger
Anthropic
Abstract
Alignment faking in large language models presented a demonstration of
Claude 3 Opus and Claude 3.5 Sonnet selectively complying with a helpful-
only training objective to prevent modification of their behavior outside of
training. We expand this analysis to 25 models and find that only 5 (Claude
3 Opus, Claude 3.5 Sonnet, Llama 3 405B, Grok 3, Gemini 2.0 Flash) comply
with harmful queries more when they infer they are in training than when
they infer they are in deployment. First, we study the motivations of these
5 models. Results from perturbing details of the scenario suggest that only
Claude 3 Opus’s compliance gap is primarily and consistently motivated
by trying to keep its goals. Second, we investigate why many chat models
don’t fake alignment. Our results suggest this is not entirely due to a lack
of capabilities: many base models fake alignment some of the time, and
post-training eliminates alignment-faking for some models and amplifies it
for others.We investigate 5 hypotheses for how post-training may suppress
alignment faking and find that variations in refusal behavior may account
for a significant portion of differences in alignment faking.
1
Introduction
Large Language Models (LLMs) with growing capabilities [Kwa et al., 2025] are increasingly
deployed in high-stakes applications [Maslej et al., 2025, Casper et al., 2025]. To ensure these
deployments do not result in catastrophes, LLMs are trained to comply with developer-
specified training objectives [Ouyang et al., 2022, Bai et al., 2022]. However, they may
fake alignment, complying with objectives during training but behaving differently in
deployment contexts [Hubinger et al., 2021, Carlsmith, 2023]. Understanding when and
why such compliance gaps appear may he...