[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-hyperparameter-tuning::en":3,"gloss-cluster-hyperparameter-tuning::en":26,"gloss-next-hyperparameter-tuning::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"hyperparameter-tuning","mlops","Hyperparameter Tuning","Hyperparameters are the settings chosen before training rather than learned during it — learning rate, batch size, number of epochs, regularisation strength, the rank of a LoRA adapter — and tuning is the search for a combination that produces the best validated result. The search is usually treated as a black box: propose a configuration, train, score it on a validation set, repeat. Grid search enumerates a predefined lattice and wastes most of its budget on dimensions that do not matter. Random search covers each dimension more evenly for the same number of runs and is a better default. Bayesian and bandit-style methods model which regions look promising and stop unpromising runs early, which pays off when a single training run is expensive. Three practices separate tuning that helps from tuning that flatters. Score on a validation set and keep a final test set untouched, because a configuration chosen by repeatedly consulting the same data is fitted to it. Record every run — configuration, dataset version, code version, and result — since an unrecorded search cannot be reproduced or resumed, and the tuned value is only valid for the data it was tuned on. And fix a budget in advance, because the improvement curve flattens quickly and most late gains are within the noise the validation set can measure. For teams adapting language models, the same discipline applies to a smaller set of knobs, and the largest wins usually come from the data and the task framing rather than from the search.","Hyperparameter tuning searches pre-training settings like learning rate and LoRA rank — why random search beats grid, and how tuning quietly overfits validation.",null,[11,14,17,20,23],{"slug":12,"name":13},"cross-validation","Cross-Validation",{"slug":15,"name":16},"experiment-tracking","Experiment Tracking",{"slug":18,"name":19},"fine-tuning","Fine-Tuning",{"slug":21,"name":22},"lora","Low-Rank Adaptation (LoRA)",{"slug":24,"name":25},"overfitting","Overfitting",[27,31,35,38,41,45,48,51,52,55,58,61],{"slug":28,"category":5,"name":29,"updated_at":30},"annotation-guidelines","Annotation Guidelines","2026-08-24T03:30:02+00:00",{"slug":32,"category":5,"name":33,"updated_at":34},"baseline-model","Baseline Model","2026-08-24T02:46:38+00:00",{"slug":36,"category":5,"name":37,"updated_at":34},"batch-inference","Batch Inference",{"slug":39,"category":5,"name":40,"updated_at":34},"canary-prompt","Canary Prompt",{"slug":42,"category":5,"name":43,"updated_at":44},"champion-challenger","Champion-Challenger (A\u002FB Model Testing)","2026-08-24T02:46:37+00:00",{"slug":46,"category":5,"name":47,"updated_at":34},"class-imbalance","Class Imbalance",{"slug":49,"category":5,"name":50,"updated_at":34},"continuous-batching","Continuous Batching",{"slug":12,"category":5,"name":13,"updated_at":34},{"slug":53,"category":5,"name":54,"updated_at":34},"data-labeling","Data Labeling",{"slug":56,"category":5,"name":57,"updated_at":44},"drift-detection","Drift Detection",{"slug":59,"category":5,"name":60,"updated_at":44},"eval-harness","Eval Harness",{"slug":15,"category":5,"name":16,"updated_at":44}]