gradient-guided

Aletheia introduces a gradient-guided layer selection method for efficient LoRA fine-tuning that identifies task-relevant transformer layers via lightweight gradient probes and applies adapters selectively, achieving 15-28% training speedup across 14 models while maintaining downstream performance on MMLU, GSM8K, and HumanEval benchmarks.