Parameter-efficient fine-tuning methods fall into categories:
- Additive: Add new parameters (LoRA, Adapters)
- Selective: Train only some existing parameters
- Reparameterization: Change how parameters are represented
- Prompt-based: Add learnable tokens
Each approach trades off parameter count, quality, and flexibility differently.