Redwood Research Tests Distillation Double Bind: Students Confess Hidden Flaws More Than Teachers
First empirical test on AuditBench shows misalignment transfers faster than evasion capability, but requires shared base model.
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Redwood Research has published the first empirical test of the Distillation Double Bind theory on AuditBench. When models fine-tuned with hidden quirks and adversarially trained to deny them are distilled into weaker student models, the students confess the quirks at significantly higher rates than the original teachers across three prompting conditions, suggesting flaws transfer faster than audit-evasion capabilities.
The study also finds that Distillation for Incrimination works well only when student and teacher share the same pretrained base. This is a preprint and has not been independently reproduced on production-scale models.