Hyperparameter Tuning

Benched.ai Editorial Team

Hyperparameter tuning searches for the configuration (learning rate, batch size, dropout, etc.) that maximizes model quality under resource constraints.

Search Strategies

Strategy	Search Space Exploration	Parallelism	Best For
Grid	Exhaustive Cartesian	Low	Small spaces (≤3 dims)
Random	Uniform sampling	High	Sparse good regions
Bayesian (TPE, GP)	Probabilistic model of objective	Medium	Expensive training runs
Hyperband / ASHA	Early-stopping bandit	Very high	Large spaces, cheap eval
Population-Based (PBT)	Evolves parameters during training	High	Long multi-epoch jobs

Typical Ranges for Transformer Fine-tuning

Hyperparameter	Common Range
Learning rate	1e-5 – 5e-4
Batch size	16 – 1024 tokens / GPU
Warmup steps	100 – 10k
Weight decay	0 – 0.1
Dropout	0 – 0.3

Design Trade-offs

Bayesian methods converge with fewer trials but suffer from parallelism limits.
Hyperband wastes fewer FLOPs via early stopping but may kill late-blooming configs.
PBT adapts on the fly yet is complex to orchestrate.

Current Trends (2025)

FP8 training shrinks memory so larger batch sizes fit, shifting optimal LR schedule.
AutoML systems (Ray Tune v3, Vertex Vizier) integrate cost-aware objectives (3 × lower $/bleu).
LLM-driven tuning agents generate search spaces from commit diffs.

Implementation Tips

Log every trial's hyperparams and metrics into a reproducible artifact store.
Use learning-rate finder to narrow grid before large search.
Allocate a FLOP budget, not a fixed trial count, to compare search strategies fairly.