Inference & Serving · 2025
LoRA Without Regret
Shows that LoRA applied to all layers with the right hyperparameters (notably a learning rate ~10x higher than full fine-tuning) matches full fine-tuning's learning efficiency and final quality across typical post-training regimes.
Editorial record
Plain-language summary
Shows that LoRA applied to all layers with the right hyperparameters (notably a learning rate ~10x higher than full fine-tuning) matches full fine-tuning's learning efficiency and final quality across typical post-training regimes.
Knowledge graph
Relationships
Descendants
Depends onEvidence: Strongly supported
LoRA / QLoRA (parameter-efficient fine-tuning)
Studies when LoRA matches full fine-tuning
TML
Source record
Provenance
- Record ID
- P-657
- Record created
- 2026-07-13
- Last reviewed
- 2026-07-14
- Record version
- 2
Citation caveat: Citation metadata is approximate and marked unverified in the source dataset.