arXiv:2605.30334v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized various fields, yet their training efficiency is heavily reliant on effective data curation. While data selection has been widely studied, the strategic data organization for enhanced training remains an underexplored area, particularly since current LLMs are often trained for only one or a few epochs. This paper systematically explores the influence of data organization on LLM training by reusing pre-computed sample-level scores originally generated for data efficiency, thereby incurring minimal a
The proliferation of LLMs and increasing computational costs are driving a need for more efficient training methodologies, making data organization a critical area of research.
Optimizing data organization can significantly reduce the computational resources and time required for LLM training, impacting the affordability and accessibility of advanced AI systems.
The focus in LLM training will shift from solely data selection to also include intelligent data organization and curation strategies, leading to more efficient model development.
- · AI research labs
- · Cloud providers with optimized data pipelines
- · LLM developers
- · Data curation platforms
- · AI companies with inefficient training pipelines
- · Commodity data suppliers
More cost-effective and faster development cycles for advanced large language models.
Democratization of LLM training as resource barriers are lowered, potentially allowing smaller players to compete.
Accelerated progress in AI capabilities across various domains due to quicker iteration and experimentation in model development.
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Read at arXiv cs.AI