arXiv:2606.30077v1 Announce Type: new Abstract: With Large Language Model (LLM) pre-training and fine-tuning shifting its focus from data volume to data quality, quality data selection has emerged as a critical research topic. Existing online data selection methods for LLM training are typically "batch-constrained", limiting optimization to local utility within random batches. To overcome this, we propose GAIA (Global Adaptive Instruction tuning via GAussian processes), a framework that formulates data valuation as a global estimation process. GAIA employs Gaussian Process regression to model
The rapid advancement of LLMs has made data quality selection a critical bottleneck, pushing researchers to innovate beyond current batch-constrained methods.
Improving online data selection for LLM instruction tuning directly impacts the efficiency and quality of AI model development, making LLMs more effective with less data.
This research introduces a novel framework that shifts data valuation from local batch optimization to global estimation, potentially accelerating the development of higher-quality AI models.
- · AI developers
- · Cloud computing providers
- · Enterprises adopting LLMs
- · Data scientists
- · Companies relying solely on data volume
- · Inefficient LLM fine-tuning methods
More efficient and effective LLM fine-tuning processes will become standard.
The cost and computational resources required for developing high-performing LLMs may decrease, democratizing access.
Smaller organizations or countries with limited data access could develop competitive AI models, potentially impacting 'sovereign-ai' efforts.
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Read at arXiv cs.LG