What is Spectrum? 

Spectrum is a cutting-edge training methodology designed to significantly optimize the training of Large Language Models (LLMs). The technique was pioneered by ML researchers Eric Hartford, Lucas Atkins, Fernando Fernandes Neto, and David Golchinfar (Atkins and Fernandes Neto work at Arcee AI, which has productized Spectrum in their model training pipeline). 

The core idea of Spectrum: selectively train only the layers of the model that matter most, based on their signal-to-noise ratio (SNR). By focusing on layers with a high SNR, which contribute most to performance improvements, and leaving the low SNR layers frozen, Spectrum can reduce training costs by as much as 50%. Additionally, this selective training approach minimizes hallucinations, as it avoids updating random layers that don’t have a significant impact. Unlike traditional methods (such as PEFT) which train random layers, Spectrum hones in on the most impactful layers to enhance both efficiency and accuracy​.