arXiv:2606.04261v1 Announce Type: cross Abstract: Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement, evaluate, and revise data policies against noisy benchmark feedback. We ask whether generalist coding agents can automate this data-curation loop. We introduce *Curation-Bench*, an agent-centric benchmark that fixes the model, training recipe, and evaluation suite while giving agents command-line access to inspect data, implement policies, submit them to a fixed training/evaluation pipeline, an
The proliferation of advanced generalist agents and the increasing labor costs associated with high-quality data curation make this an opportune time to explore automated solutions.
Automating data curation impacts the efficiency, cost, and quality of AI development, potentially accelerating progress and broadening access to advanced AI capabilities.
The labor-intensive and iterative process of data curation can now be significantly streamlined through autonomous agents, reducing bottlenecks in AI model training.
- · AI developers
- · Companies with large datasets
- · Generalist agent developers
- · AI-reliant industries
- · Manual data labeling services
- · Inefficient AI development pipelines
Significant reduction in time and resources required for AI model development and deployment.
Increased speed of AI innovation and a wider range of applications as data quality and availability improve.
Shifting of human effort from data preparation to higher-level AI research and ethical oversight, leading to more sophisticated and potentially safer AI systems.
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