5 Fine-tuning Techniques Explained: LoRA, VeRA, and More
techNmak · x · 2026-08-27
A deep dive into 5 parameter-efficient fine-tuning (PEFT) techniques and how they modify model weights: 1) LoRA: Freezes W, trains matrices A and B. 2) LoRA-FA: Freezes W and A, trains only B. 3) VeRA: Freezes A and B (randomly initialized), trains only two tiny scaling vectors. 4) Delta-LoRA: Allows W to evolve via low-rank delta propagation. 5) LoRA+: Same structure as LoRA but assigns a higher learning rate to B than A. The core idea is determining the minimum required parameters for model adaptation.
More from Research
- Study Uses Modified tRNAs to Rescue Nonsense Mutations in Cystic Fibrosis — anshulkundaje · 2026-08-28
- vLLM benchmarks MTP, EAGLE-3, and other speculative decoding methods on AMD GPUs — vllm_project · 2026-08-28
- Robotics evolution: Mass production and RL whole body control are key — chris_j_paxton · 2026-08-28
- Agent-Core Spec: Defining kernel safety and task contracts for agents — BLUECOW009 · 2026-08-28
- Google open sources SAM: Sovereign Agent Mesh framework — adnan_hashmi · 2026-08-28
- Marin 535B-A23B Training at 7%, Team to Share Design Decisions and Tradeoffs — bariskasikci · 2026-08-28