Retraining

Retraining updates (Parametric Update) a deployed model on newer or additional data to restore performance degraded by distribution shift, representing the foundational lifecycle maintenance operation across all model types. Its universality across DL, FM, LLM, ML, MLLM reflects reflects the fact that distributional drift is an architecture-agnostic failure mode: any model whose training distribution diverges from its deployment distribution will degrade, regardless of architectural family ^[65]. The critical D2 (Goal) divergence from (Full/Partial) FT is precise: both operate identically on D1 (Mechanism) and D4 (Persistence), yet retraining preserves the model's original task objective (Drift Remediation) rather than reshaping its capability boundaries for a new task (Task Specialization). This D2 (Goal) distinction carries a direct validation consequence—a retraining event that strictly adheres to the original task specification does not require an intended-use review; an FT event does. The Sequential / Incremental coordinate reflects that retraining data arrives in temporally ordered batches whose distributional properties evolve across the model's operational lifecycle—this is structurally distinct from the static, curated datasets of (Full/Partial) FT (Large Labeled, Small Labeled) or FSL (; Few Demonstrations, Small Labeled). The Ad-hoc Permanent, Scheduled Permanent coordinate marks the boundary with CL: unconstrained retraining overwrites prior weight states with a new fixed checkpoint, whereas CL's Bounded Cumulative, Unbounded Cumulative encodes a commitment to accumulation rather than replacement. The Whole-Model scope means that every weight is overwritten at each retraining event; there is no modular isolation boundary, so any behavioral drift introduced by the new training data propagates across all task dimensions simultaneously—necessitating automated full-system regression testing against a frozen behavioral baseline before re-deployment, with the historical checkpoint retained as the rollback artifact.