arXiv:2606.08574v1 Announce Type: new Abstract: Data pruning (DP), as an oft-stated strategy to alleviate heavy training burdens, reduces the volume of training samples according to a well-defined pruning method while striving for near-lossless performance. However, existing approaches, which commonly select highly informative samples, can lead to biased gradient estimation compared to full-dataset training. Furthermore, the analysis of this bias and its impact on final performance remains ambiguous. To address these challenges, we propose OrderDP, a plug-and-play framework that aims to obtain
The rapid increase in AI model complexity and data volume makes efficient training a critical constraint, driving research into methods like data pruning to manage computational resources.
Improving data pruning techniques can significantly reduce the computational burden and energy consumption associated with training large AI models, impacting the efficiency and accessibility of advanced AI development.
This research introduces a theoretically guaranteed method for data pruning, potentially leading to more reliable and less biased ways of optimizing AI training processes without sacrificing performance.
- · AI model developers
- · Cloud computing providers
- · Data scientists
- · Energy-efficient AI initiatives
- · Current inefficient data training methods
- · Organizations with limited compute resources (if not adopted)
Reduced training costs and time for AI models, allowing for faster iteration and development cycles.
Democratization of advanced AI research by lowering the computational barrier to entry.
Accelerated development of more complex and specialized AI models across various sectors due to improved resource efficiency.
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Read at arXiv cs.LG