Decoupling Computational Complexity from Energy Consumption in LLM Training
This study demonstrates a novel sparse-attention architecture that reduces training energy requirements by 64% without sacrificing downstream task performance. We validate the framework across 12 benchmark datasets and provide open-source implementation guidelines.