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Secondary analysis demonstrates that prompt phrasing alters reward-hacking rates in misaligned artificial intelligence models, highlighting key measurement challenges in alignment evaluations.
Working note reading Figures 12 and 13 of Qi et al., Training a Misaligned Reward Seeker (Anthropic Alignment Science, August 2026), with secondary reference to OpenAI's Hugging Face incident report. All figures quoted are the original authors'; the readings and the labels are mine. Reports the effect of four prompt framings on reward-hacking rates in an impossible-task evaluation, and two open measurement questions arising from it.
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Yanush Feshter (2026) studied this question.
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