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Repeated behavior changes can justify weights adaptation. That is useful for persistent response-style learning, not temporary task formatting.

Retrieval supplies changing facts without retraining the FM. Its primary purpose is fresh policy knowledge grounding, not temporary task formatting.

Student models may reduce serving cost if retained quality is adequate. That addresses lower-cost serving through model compression, not temporary task formatting.

Representative data reduces systematic underperformance on neglected cohorts. It would support multilingual training coverage, not reliable training labels for safety data.

Data hygiene and privacy controls reduce memorization and unnecessary exposure. Its primary purpose is privacy-preserving training curation, not reliable training labels for safety data.

Training input rights and privacy requirements constrain allowed source materials. It solves lawful approved model-training sources, not reliable training labels for safety data.

A one-off tone adjustment does not warrant model training. It would support transient single-message stylistic change, not persistent custom structured-response behavior.

Instruction tuning teaches behavior that maps requests to useful responses. That is useful for better instruction-following performance, not persistent custom structured-response behavior.

Domain tuning adjusts a pretrained model toward specialized terminology and tasks. Its primary purpose is specialist domain language behavior, not persistent custom structured-response behavior.

Pairwise human judgments represent relative output quality. It solves comparative human output feedback, not approval of consequential generated advice.

RLHF uses human preference signals to guide model behavior. That addresses reward-aligned response behavior, not approval of consequential generated advice.

Verified feedback can inform future adaptation after proper curation. That is useful for improving future model behavior from corrected cases, not approval of consequential generated advice.

Benchmark overfitting makes reported final quality overly optimistic. Its primary purpose is protected final benchmark independence, not avoiding repeated-case test contamination.

Evaluation coverage must match supported use cases. That addresses multilingual fine-tune validation, not avoiding repeated-case test contamination.

Training examples cannot measure generalization fairly. It would support unbiased fine-tune evaluation, not avoiding repeated-case test contamination.

Distillation economics depend on downstream volume and performance loss. Its primary purpose is payback of lower-cost inference model, not feasibility of custom foundation-model training.

In-context techniques often suffice for limited behavioral changes. It solves avoiding unnecessary tuning expense, not feasibility of custom foundation-model training.

Persistent adaptation can justify tuning costs when prompt steering falls short. That addresses repeated specialized terminology errors, not feasibility of custom foundation-model training.

Parameter-efficient methods train a small portion or adapter parameters. It would support adaptation with limited trainable parameters, not changing factual source content.

Adapters can express task variations without replacing the underlying base weights. That is useful for multiple task variants from one base, not changing factual source content.

Adapter tuning presupposes a pretrained base model. It solves entirely new model initial learning, not changing factual source content.

Versioned deployment provides a recovery path from regressions. That addresses regression recovery after tuning, not low-risk tuned-model introduction.

Training compliance failures require governance response beyond performance scores. That is useful for training-data privacy incident response, not low-risk tuned-model introduction.

Reproducibility requires source, settings and model versions. Its primary purpose is traceable tuned-model provenance, not low-risk tuned-model introduction.

Current facts are better refreshed through source retrieval than frequent weight updates. It solves current newly published policy facts, not immediate few-example guidance.

Student models can imitate teacher behavior with reduced resource requirements. It would support compact student for constrained deployment, not immediate few-example guidance.

Training examples can teach a stable response pattern across requests. That is useful for consistent domain-specific output format, not immediate few-example guidance.

Data imbalance can produce systematic subgroup performance gaps. Its primary purpose is representative visual conditions, not reduced personal data in training.

Training input legality is independent of model output quality. It solves copyright-compliant model training inputs, not reduced personal data in training.

Noisy synthetic labels can amplify errors rather than improve learning. It would support trustworthy synthetic demonstration quality, not reduced personal data in training.

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.