Zenodo

A research paper exploring parameter space division techniques for large language model training. The work addresses limitations in current LLM architecture, arguing that direct, appropriate parameter space division is a key bottleneck. It examines why existing approaches like orthogonality and mixture-of-experts methods fall short.

The paper proposes algebraic methods as an alternative approach to parameter space division in LLM training, offering a new perspective on optimization and model architecture design.