FT (partial)

Partial FT targets Task Specialization via the same Parametric Update mechanism as Full FT but constrains updates to later, task-specific layers while freezing the early layers that encode broadly transferable representations ^[222]. This architectural discipline collapses the update surface from Whole-Model to Partial—a coordinate divergence from Full FT that carries both a data consequence and a validation consequence: the data requirement contracts to Small Labeled (the frozen layers perform feature extraction, leaving only the task-specific layers requiring supervision), and the regression testing footprint is structurally isolated to the modified layer subset, enabling targeted component-level rollback. Compared to PEFT, which introduces a fully decoupled Modular adapter that can be removed without touching any base-model layer, Partial FT embeds updates directly into the base model's later layers—rollback requires layer-level checkpoint restoration, not a clean module swap. The practical distinction matters for change-control documentation: a PEFT event generates a standalone adapter artifact; a Partial FT event generates a modified base-model checkpoint whose changed layers must be explicitly versioned. Partial FT necessitates versioned checkpointing of the modified layer subset and localized parametric regression testing bounded strictly to those layers; because the frozen early layers remain mathematically unchanged, they require no re-validation.