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(Some) Natural Emergent Misalignment from Reward Hacking in Non-Production RL

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Text177 passages · 2 marked

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Authors: Satvik Golechha*, Sid Black*, Joseph Bloom2 passages
Executive Summary12 passages · 1 marked

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).

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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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A – Unfaithful Chain-of-Thought during RL5 passages
B – Improving misalignment evals for open models10 passages
C – Inoculation prompting3 passages
D – Sweep over KL penalty and hack hint prompts8 passages
E – Reproducing the original EM paper11 passages
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53 Concrete Evaluations to Investigate the OpenAI Model That Hacked Hugging Face0 passages

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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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(Some) Natural Emergent Misalignment from Reward Hacking in Non-Production RL
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Current Reward Hacking Model Organisms Don’t Hack Like Real Modelsunknown1 claimReanalyzes its data, on 1 claim.

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Link
https://www.alignmentforum.org/posts/2ANCyejqxfqK2obEj/some-natural-emergent-misalignment-from-reward-hacking-in
Archived copy
https://web.archive.org/web/20260812013355/https://www.alignmentforum.org/posts/2ANCyejqxfqK2obEj/some-natural-emergent-misalignment-from-reward-hacking-in
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