This research highlights the efficiency of Alternating Gradient Descent in multi-task training, particularly by reducing padding requirements through intelligent task grouping based on sequence lengths.
By clustering tasks that share similar sequence lengths, we significantly minimize the amount of padding required, enhancing the overall computational efficiency during training.
Our experiments demonstrate that the proposed method compares favorably against state-of-the-art techniques, showcasing the advantages of structured task management and optimized resource use.
The findings implicate that proper tokenization combined with Alternating Gradient Descent can unlock new potentials in diverse applications like language modeling and video generation.
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