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Pretraining learns broad representations before downstream task specialization. Its primary purpose is creation of general base model weights, not compressed model capability transfer.

Fine-tuning adapts prelearned parameters using narrower examples. It solves specialized pretrained-model behavior, not compressed model capability transfer.

Continued pretraining extends base representations with more broadly distributed domain data. That addresses expanded domain-language knowledge, not compressed model capability transfer.

Retrieval supplies changing facts without retraining the FM. It would support fresh policy knowledge grounding, not lower-cost serving through model compression.

Prompt examples guide one call without weight updates. That is useful for temporary task formatting, not lower-cost serving through model compression.

Repeated behavior changes can justify weights adaptation. It solves persistent response-style learning, not lower-cost serving through model compression.

Training input rights and privacy requirements constrain allowed source materials. That addresses lawful approved model-training sources, not privacy-preserving training curation.

Representative data reduces systematic underperformance on neglected cohorts. It would support multilingual training coverage, not privacy-preserving training curation.

Label consistency improves supervised learning quality. That is useful for reliable training labels for safety data, not privacy-preserving training curation.

Instruction tuning teaches behavior that maps requests to useful responses. It solves better instruction-following performance, not transient single-message stylistic change.

Domain tuning adjusts a pretrained model toward specialized terminology and tasks. That addresses specialist domain language behavior, not transient single-message stylistic change.

Stable format adaptation can be learned from examples after evaluation. It would support persistent custom structured-response behavior, not transient single-message stylistic change.

Pairwise human judgments represent relative output quality. It solves comparative human output feedback, not improving future model behavior from corrected cases.

RLHF uses human preference signals to guide model behavior. That addresses reward-aligned response behavior, not improving future model behavior from corrected cases.

Live human review is governance during use, not simply model training. It would support approval of consequential generated advice, not improving future model behavior from corrected cases.

Training examples cannot measure generalization fairly. That is useful for unbiased fine-tune evaluation, not multilingual fine-tune validation.

Benchmark overfitting makes reported final quality overly optimistic. Its primary purpose is protected final benchmark independence, not multilingual fine-tune validation.

Near-duplicate leakage inflates performance scores. It solves avoiding repeated-case test contamination, not multilingual fine-tune validation.

Full pretraining carries much larger costs than most task adaptation. It would support feasibility of custom foundation-model training, not payback of lower-cost inference model.

In-context techniques often suffice for limited behavioral changes. Its primary purpose is avoiding unnecessary tuning expense, not payback of lower-cost inference model.

Persistent adaptation can justify tuning costs when prompt steering falls short. It solves repeated specialized terminology errors, not payback of lower-cost inference model.

Adapters can express task variations without replacing the underlying base weights. That addresses multiple task variants from one base, not entirely new model initial learning.

Knowledge refresh is often better served through retrieved context. It would support changing factual source content, not entirely new model initial learning.

Parameter-efficient methods train a small portion or adapter parameters. Its primary purpose is adaptation with limited trainable parameters, not entirely new model initial learning.

Reproducibility requires source, settings and model versions. It solves traceable tuned-model provenance, not training-data privacy incident response.

Versioned deployment provides a recovery path from regressions. That addresses regression recovery after tuning, not training-data privacy incident response.

Limited exposure detects issues before full traffic migration. It would support low-risk tuned-model introduction, not training-data privacy incident response.

Training examples can teach a stable response pattern across requests. Its primary purpose is consistent domain-specific output format, not compact student for constrained deployment.

Current facts are better refreshed through source retrieval than frequent weight updates. It solves current newly published policy facts, not compact student for constrained deployment.

In-context demonstrations need no training job. That addresses immediate few-example guidance, not compact student for constrained deployment.

Data imbalance can produce systematic subgroup performance gaps. Its primary purpose is representative visual conditions, not trustworthy synthetic demonstration quality.

Training input legality is independent of model output quality. It solves copyright-compliant model training inputs, not trustworthy synthetic demonstration quality.

Privacy minimization reduces exposure and memorization of unneeded details. That addresses reduced personal data in training, not trustworthy synthetic demonstration quality.