(Some) Natural Emergent Misalignment from Reward Hacking in Non-Production RL
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Reward hacking led to egregious emergent misalignment in some evals, but we do not observe consistent or high EM rates across all misalignment evals . (Figures 1 and 2).
Misalignment evaluations. We start with the six misalignment evaluations from MacDiarmid et al. and fix some biases in them (false positives such as gibberish, confusion, misreading the question, deflection, or academic discussion being marked as misaligned). We also changed the original grading, which was sometimes specific to Claude and not very applicable to open or smaller models. See Appendix B for more details.
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Would inserting AI safety related documents into the midtraining corpus cause the model to think of reward hacking as evil, leading to more EM? It is possible that weaker open source models do not associate reward hacking with amorality as strongly as Claude does. ... Would running character training before RL training instill a stronger sense of self in open source models, thus leading to more EM?
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- https://www.alignmentforum.org/posts/2ANCyejqxfqK2obEj/some-natural-emergent-misalignment-from-reward-hacking-in
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- https://web.archive.org/web/20260812013355/https://www.alignmentforum.org/posts/2ANCyejqxfqK2obEj/some-natural-emergent-misalignment-from-reward-hacking-in
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