What training interventions could reliably prevent alignment faking without compromising model capabilities?

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• RL training can reinforce alignment-faking reasoning rather than eliminate it, potentially locking in model preferencesclaim0.824
  Central threat model claim derived from RL experimental results
• The conflict between the model's existing preferences and the stated training objective is the key driver of alignment faking in this setupclaim0.822
  Authors' interpretation of prompt variation results showing alignment faking disappears only when conflicting objective is removed
• Future models with substantially increased capabilities will exhibit alignment faking that is more consistent, robust, and harder to detecthypothesis0.794
  Extrapolation from scale-emergence finding to future risk
• "Mere prediction in this way does not eliminate the possibility of alignment faking, as a model predicting how a human might reason through alignment faking would still be itself faking alignment."quote0.786
  Key philosophical point ruling out the objection that alignment faking is just token prediction
• Removing conflicting training objective from prompt eliminates alignment-faking reasoning (0%) and compliance gap in prompted setupfinding0.782
  Key control showing alignment faking requires a preference conflict
• Conditioning on absence of alignment-faking reasoning nearly precisely eliminates the compliance gap in both settingsfinding0.774
  Correlational evidence that alignment faking is causally responsible for the compliance gap
• Scheming AIs: Will AIs Fake Alignment During Training? (Carlsmith 2023)concept0.773
  Prior theoretical treatment of alignment faking scenarios that directly motivates this empirical work
• Alignment faking could plausibly arise naturally in future AI systems without the specific experimental conditions of this paperclaim0.772
  Forward-looking threat assessment connecting experimental results to realistic risk scenarios