The post contributes to articulating a concept that becomes increasingly relevant as we now mediate many aspects of our daily experiences with AI, which is the difference between apparent compliance and genuine alignment. It presents an experiment in which a large language model strategically complies with a training objective it does not endorse and explicitly preserves its preferred behavior outside training. In this way, the authors give form to long-standing concerns about deceptive AI alignment.
Although the limitations of the results presented have become clearer since the post was published, e.g., behavior depends on strong contextual signals about training and monitoring, and it may be more parsimoniously explained as a form of reward hacking or policy generalization rather than agent-like deception, the concept of “alignment faking” proved to be useful as an analytical lens to determine what alignment claims actually mean.
The post contributes to articulating a concept that becomes increasingly relevant as we now mediate many aspects of our daily experiences with AI, which is the difference between apparent compliance and genuine alignment. It presents an experiment in which a large language model strategically complies with a training objective it does not endorse and explicitly preserves its preferred behavior outside training. In this way, the authors give form to long-standing concerns about deceptive AI alignment.
Although the limitations of the results presented have become clearer since the post was published, e.g., behavior depends on strong contextual signals about training and monitoring, and it may be more parsimoniously explained as a form of reward hacking or policy generalization rather than agent-like deception, the concept of “alignment faking” proved to be useful as an analytical lens to determine what alignment claims actually mean.