HUR Architecture with HurNet: Revolutionizing Language Model Training through Single-Step Weight Optimization

Abstract

This paper introduces HurNet, a novel neural network architecture that revolutionizes the training of large language models by replacing iterative backpropagation with direct, single-step division-based computations. By intelligently initializing weights using pseudo-inverse or direct division methods, HurNet achieves optimal parameter configurations more rapidly than traditional gradient descent approaches. The proposed Hur Architecture integrates HurNet with Transformer-based models, enabling efficient training on modest hardware with reduced computational costs. Key features include automatic hyperparameter configuration, customizable fine-tuning modalities, mixture of experts integration, model merging, and transfer learning capabilities. Additionally, Hur Architecture supports infinite context windows through a sophisticated embedding synthesis mechanism formalized via probabilistic concatenation operators. Empirical evaluations demonstrate significant speedups, with Hur models outperforming conventional GPT architectures by up to 582% in fine-tuning tasks. The resulting models are serialized in the .hurlm format, facilitating deployment across resource-constrained environments.

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