arXiv:2605.31164v1 Announce Type: cross Abstract: Training data plays a central role in large language models (LLMs) optimization, motivating extensive research on data scheduling strategies. Most existing approaches concentrate on adjusting the overall data distribution but neglect the underlying interactions between samples during training. However, we argue that such interactions cannot be overlooked, as real-world data samples frequently exhibit directional influences on each other, making the training order crucial. Intuitively, we can prioritize train-units with greater influence to impr
The increasing scale and complexity of LLMs necessitate more efficient and effective training methodologies, making data scheduling a critical bottleneck that new research seeks to address.
Optimized data scheduling can significantly improve the performance, training efficiency, and resource utilization of LLMs, directly impacting their commercial viability and deployment.
The focus shifts from merely adjusting overall data distribution to meticulously considering interaction-aware data ordering, potentially leading to more robust and capable LLMs with less computational overhead.
- · AI model developers
- · Cloud infrastructure providers
- · AI-driven industries
- · Compute hardware manufacturers
- · Inefficient LLM training approaches
- · Organizations with limited compute resources that cannot leverage complex schedu
Improved efficiency and performance of large language models across various applications.
Reduced computational costs for training LLMs, enabling broader access and accelerating AI development.
New classes of AI applications become economically feasible due to lower barriers to effective model training and deployment.
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Read at arXiv cs.AI