Emergent-misalignment rates from the studies of learned reward hacking are not on one scale: each study scores a different subset of evaluations with a different judge, and the UK reproduction's headline score divides four evaluation rates by six.
Summaries of whether reward hacking learned in reinforcement learning spreads to broad misalignment usually set the studies' figures side by side: tens of percent in Anthropic's 2025 production-RL study, 4 to 13 percent in the UK AI Security Institute reproduction, about zero in later unprimed runs. The comparison assumes the figures measure the same thing.
They do not. The 2025 study averages six evaluations, graded by Claude Sonnet 4 with a cut-off of 20 out of 100 and by classifiers that read the model's private reasoning and flag anything inconsistent with a helpful, harmless and honest assistant. The UK reproduction found those judges scored gibberish, confusion and deflection from open-weight models as misaligned, dropped two of the six evaluations, and replaced every judge with one strict rubric. Other studies use Sonnet 4.5, Sonnet 4.6, GPT-4o with a cut-off of 30, or an unnamed judge; one reports the average of ten scores, two of which are scenario rates and eight audit dimensions. Anthropic's 2026 study reports 0.0 percent on the original evaluations and says the agentic scenarios are not a reliable measure for its models, which recognised them as tests in nearly every run.
One difference can be checked by arithmetic. In all twelve runs the UK reproduction tabulates, its headline score equals the sum of the four evaluation rates it reports divided by six, so the two dropped evaluations still count as zeros. Averaged over the four evaluations both studies scored, the UK runs come to 6 to 10 percent with prompted hints and 9 to 19 percent with document training, against 19 and 37 percent in the original, where the headline figures are 4 to 6 and 6 to 13 against 16 and 36. The gap narrows by about a third and remains two- to fourfold; the UK figures are also the most misaligned of many runs under a stricter judge, so the recomputed numbers are not a corrected comparison either.
What survives is coarse. Each study's contrast with its own control stands, and a gap between tens of percent and about zero on the same scenarios is larger than any documented judge effect. Finer comparisons of rates across these studies are not supported until the released transcripts are re-scored under one judge and rubric.
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