Fine-tuning can be likened to imposing a kind of censorship on the simulator; it leaves the underlying range of roles essentially the same but compromises authenticity

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Fine Tuning and Adaptationconcept0.807
  The patient, hand-guided adjustment of shape and dimension to each unique condition in a building; requires materials that make it economical and easy.
• The role-play framing remains applicable in the context of fine-tuning; taking literally a fine-tuned agent's apparent self-preservation desire is no less problematic than with an untuned base modelhypothesis0.802
  Extension of role-play framework to fine-tuned models, resisting the idea that RLHF changes the fundamental nature of simulacra
• Fine-tuning induces the behavioral pattern of self-correction but does not improve the underlying ability to correct effectivelyclaim0.800
  Key interpretive conclusion from the dissociation between attempt rate and improvement rate in fine-tuning experiments
• 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 preferenceshypothesis0.795
  Future work hypothesis about extending SOO to direct value alignment
• SOO fine-tuning preserves useful self-other distinctions necessary for task performance despite inducing overlapclaim0.795
  Claim supported by Perspectives scenario results showing near-100% accuracy post-fine-tuning
• Fine-tuningconcept0.791
  Parameter updates that reduce mismatch dr; another anchoring variant in UCCT.
• Fine-Tuning via Reinforcement Learningmethod0.788
  Technique used to impose guardrails on base LLMs, analogized to censorship on the simulator's range of simulacra
• Roleplay Fine-Tuningconcept0.788
  Fine-tuning for persona depth and emotional performance; actively suppresses self-observation