Fine-tune with 0.01% parameters: BEFT method accepted at ACL 2026

Researchers from Lund University and Google DeepMind propose BEFT, a bias-only fine-tuning method that trains only the value bias b(v), achieving near full fine-tuning performance with just 0.01% of parameters in low-data settings. The work has been accepted at ACL 2026 and integrated into the Hugging Face PEFT library, offering an efficient solution for resource-constrained fine-tuning.