Fine-tuning reduces mismatch dr, retrieval increases effective cohesion ρd, and few-shot adjusts the budget k

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• Fine-tuning reduces dr; retrieval increases effective ρd; few-shot k trades budget against bothhypothesis0.912
  UCCT's unified view of adaptation methods
• Hypothesis: Fine-tuning reduces mismatch dr between prior and targethypothesis0.870
  UCCT's theoretical prediction about how fine-tuning maps onto the anchoring score
• SOO fine-tuning could be extended to align AI representations of its own goals with human user preferences, reducing misalignment by fostering coherence between self-related and other-related preferenceshypothesis0.816
  Future work hypothesis about extending SOO to direct value alignment
• Fine-tuning induces the behavioral pattern of self-correction but does not improve the underlying ability to correct effectivelyclaim0.804
  Key interpretive conclusion from the dissociation between attempt rate and improvement rate in fine-tuning experiments
• SOO fine-tuning could complement RLHF and Constitutional AI by fostering internal coherence that promotes honest behaviorsclaim0.798
  Integration claim positioning SOO as additive to existing alignment approaches
• SOO fine-tuning preserves useful self-other distinctions necessary for task performance despite inducing overlapclaim0.788
  Claim supported by Perspectives scenario results showing near-100% accuracy post-fine-tuning
• Shot midpoints follow k50 ∝ dr/ρd; higher cohesion and lower mismatch yield fewer required examplesclaim0.788
  Core quantitative prediction of UCCT validated by E2 threshold ordering
• Fine-tuned behaviors are low-rank in weight space, potentially making them easier to manipulate with steering vectors compared to naturalistic behaviorclaim0.780
  Cited from Wang et al. 2025a as reason SDF is preferred over demonstration fine-tuning for realistic model organisms.