Character training is more robust to adversarial prompting than activation steering on average

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• Character training is more robust to adversarial prompting than constraining system promptsclaim0.899
  Main robustness claim supported by classifier performance experiments
• Character training produces more coherent responses than activation steering because it learns a distribution over desired personas rather than forcing low-probability tokensclaim0.885
  Coherence advantage claim with mechanistic speculation about why steering leads to incoherence
• Character training for misalignment produces more subtle and realistic malicious behavior than fine-tuning on insecure code or activation steeringclaim0.848
  Realism advantage claim with safety implications; supported by anecdotal comparison in Appendix D
• Character training beats activation steering in coherence win rate 78.4% ± 5.2% on Llama 3.1 8Bfinding0.823
  Coherence comparison against steering baseline for Llama model
• Activation steering requires vastly different steering constants per model (0.7, 4.0, 525.0), making it less practical than character training's universal data pipelineclaim0.821
  Practical disadvantage of activation steering highlighted as a drawback
• Character training alters the assistant's default behavior rather than adding a superficial role-play layerclaim0.797
  Core claim distinguishing character training from role-playing, evidenced by robustness to break-character instructions
• Character training operates holistically on the persona, suppressing intuitively opposing traits as well as boosting desired onesclaim0.796
  Claim that models learn the spirit of the constitution, not just its letter, evidenced by suppression of opposing traits
• Character training changes the manner of responses rather than their informational contentclaim0.785
  Central claim demonstrated by Figure 1 where all responses constitute refusal but are conveyed differently