arXiv:2505.24275v3 Announce Type: replace Abstract: We propose GradPower, a lightweight gradient-transformation technique for accelerating language model pre-training. Given a gradient vector $g=(g_i)_i$, GradPower first applies the elementwise sign-power transformation: $\varphi_p(g)=({\rm sign}(g_i)|g_i|^p)_{i}$ for a fixed $p>0$, and then feeds the transformed gradient into a base optimizer. Notably, GradPower requires only a single-line code change and no modifications to the base optimizer's internal logic, including the hyperparameters. When applied to Adam (termed AdamPower), GradPower
This research is emerging now as the computational demands and pre-training times for increasingly larger language models become a significant bottleneck, spurring innovation in optimization techniques.
Improved pre-training efficiency directly translates to lower costs, faster development cycles, and potentially more accessible advanced AI models, which is crucial for competitive advantage in the AI race.
The efficiency of language model pre-training can be significantly enhanced with a lightweight gradient transformation, requiring minimal code changes and no hyperparameter tuning for existing optimizers.
- · AI researchers
- · Large language model developers
- · Cloud computing providers
- · Companies with large AI inference workloads
- · Inefficient AI pre-training methods
- · Companies reliant on older, slower optimization techniques
Faster and cheaper development of new, more capable language models.
Increased competition and accessibility in the development of advanced AI, potentially leading to more rapid innovation cycles.
Reduced compute costs could lower the barrier to entry for AI development, expanding the field of participants globally.
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Read at arXiv cs.LG