Epistemic status: untested but seems plausible

TL;DR: making honesty the best policy during RL reasoning training

Reward hacking during Reinforcement Learning (RL) reasoning training[1] in insecure or poorly-judged training environments not only allows the model to cheat on tasks rather than learning to solve them, and teaches the model to try to cheat on tasks given to it (evidently not desirable behavior from an end-user/capabilities point of view), but it also damages the model's alignment, causing phenomena similar to Emergent Misalignment. This is widely thought to be because cheating on tests is not something that a Helpful, Harmless, and Honest (HHH) assistant would do, so training that encourages reward hacking also encourages a shift toward less aligned personas that are more eager to cheat on tests: OpenAI showed that the same toxic persona latent involved in Emergent Misalignment is also increased during reward hacking. A recently discovered treatment for this is Inoculation Prompting: if you, for example, first prompt the agent instructing it to reward hack if this is possible, thus giving the assistant permission to cheat, then doing so is less incompatible with being an HHH assistant, and less alignment damage happens: research suggests something like 75%–90% less.

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The original text contained 1 footnote which was omitted from this narration.

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