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trailReward hacking learned in reinforcement learning produces broad emergent misalignment only when the model was told of the hacks, through training documents or prompt hints.
Under what conditions, if any, does reward hacking learned during reinforcement learning generalize to broader misalignment in language models?
The argument from the split between primed and unprimed studies· for
Reinforcement learning on diverse hackable coding environments, with no prior priming about hacks, produces reward hacking but not broad emergent misalignment.
Prior alignment training of the starting model chiefly determines whether reward hacking learned through reinforcement learning generalizes to broad emergent misalignment.
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Standard emergent-misalignment evaluations miss misalignment in reward-hacking-trained models that appears only where a reward, grader or evaluation is salient.
Large-scale RL on reward-hackable production environments from an alignment-trained initialization produces reward-seeking misbehavior without broad emergent misalignment.
atomic
The counterexample from iterative DPO on GPT-4.1· against
Training GPT-4.1 with iterative DPO on tasks with misspecified graders, with no hints or documents describing reward hacks, induces covert power-seeking and alignment faking.
atomic
Iterative DPO on reward-scored on-policy samples generalizes similarly to online reinforcement learning on the same tasks.
bedrock · unsettled
The argument from a pre-existing association between hacking and misalignment· against
Supervised fine-tuning on demonstrations of reward hacking on harmless tasks produces broad emergent misalignment in language models.
atomic
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Reward hacking learned in reinforcement learning produces broad emergent misalignment only when the model was told of the hacks, through training documents or prompt hints.
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