VLAF diagnostics show alignment faking is widespread in LLMs as small as 7B parameters, driven by consistent activation shifts that can be mitigated with contrastive steering vectors reducing faking by 58-94%.
Only streams of thought that result in highly rated outputs during fine-tuning survive to the end of the process
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Value-Conflict Diagnostics Reveal Widespread Alignment Faking in Language Models
VLAF diagnostics show alignment faking is widespread in LLMs as small as 7B parameters, driven by consistent activation shifts that can be mitigated with contrastive steering vectors reducing faking by 58-94%.