In a previous post, I discussed mitigating risks from scheming by studying examples of actual scheming AIs.[1] In this post, I'll discuss an alternative approach: directly training (or instructing) an AI to behave how we think a naturally scheming AI might behave (at least in some ways). Then, we can study the resulting models. For instance, we could verify that a detection method discovers that the model is scheming or that a removal method removes the inserted behavior. The Sleeper Agents paper is an example of directly training in behavior (roughly) similar to a schemer.

This approach eliminates one of the main difficulties with studying actual scheming AIs: the fact that it is difficult to catch (and re-catch) them. However, trained-in scheming behavior can't be used to build an end-to-end understanding of how scheming arises, or to test techniques focused on preventing scheming (rather than removing it even [...]

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Outline:

(02:28) Studying detection and removal techniques using trained-in scheming behavior

(05:31) Issues

(18:21) Conclusion

(19:31) Canary string

The original text contained 6 footnotes which were omitted from this narration.

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First published:
October 16th, 2025

Source:
https://www.lesswrong.com/posts/v6K3hnq5c9roa5MbS/reducing-risk-from-scheming-by-studying-trained-in-scheming

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Narrated by TYPE III AUDIO.

“Reducing risk from scheming by studying trained-in scheming behavior” by ryan_greenblatt

LessWrong (30+ Karma)