How does light fine-tuning modulate ρd and dr and trade off in-distribution gains against OOD robustness?

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• (iii) how does light fine-tuning modulate these terms and trade off in-distribution gains against out-of-distribution robustness?question0.881
  Third research question in E2
• Fine-tuning reduces dr; retrieval increases effective ρd; few-shot k trades budget against bothhypothesis0.780
  UCCT's unified view of adaptation methods
• Hypothesis: Fine-tuning reduces mismatch dr between prior and targethypothesis0.762
  UCCT's theoretical prediction about how fine-tuning maps onto the anchoring score
• Fine-tuning with synthetic introspective data provides additional gains in robustness of trait expression beyond distillation aloneclaim0.761
  Key claim about the value of the introspection stage, supported by both prefill attack and adversarial prompting experiments
• In-distribution re-alignment more effectively reverses the original fine-tuning procedure, while out-of-distribution re-alignment mainly suppresses misalignment generalizationclaim0.747
  Code-realigned model writes less insecure code than health-realigned model after identical steps
• Fine-tuning reduces mismatch dr, retrieval increases effective cohesion ρd, and few-shot adjusts the budget kclaim0.747
  Unified interpretation of different adaptation methods via UCCT terms
• SOO fine-tuning could complement RLHF and Constitutional AI by fostering internal coherence that promotes honest behaviorsclaim0.726
  Integration claim positioning SOO as additive to existing alignment approaches
• Dense but off-task anchors yield high ρd AND high dr; behavior does not improve, consistent with mismatch dominating Sfinding0.724
  E3 negative control validating that both ρd AND dr must be favorable for S to exceed Sc