Research Paper#Data Curation, LLMs, Proxy Models, Training Efficiency🔬 ResearchAnalyzed: Jan 3, 2026 09:25
Small Training Runs for Data Curation: A Reliability Analysis
Published:Dec 30, 2025 23:02
•1 min read
•ArXiv
Analysis
This paper addresses a crucial issue in the development of large language models (LLMs): the reliability of using small-scale training runs (proxy models) to guide data curation decisions. It highlights the problem of using fixed training configurations for proxy models, which can lead to inaccurate assessments of data quality. The paper proposes a simple yet effective solution using reduced learning rates and provides both theoretical and empirical evidence to support its approach. This is significant because it offers a practical method to improve the efficiency and accuracy of data curation, ultimately leading to better LLMs.
Key Takeaways
- •Fixed training configurations for proxy models can lead to inaccurate data quality assessments.
- •The optimal training configuration is data-dependent.
- •Using reduced learning rates for proxy model training improves the reliability of small-scale experiments.
- •This approach correlates well with fully tuned large-scale LLM pretraining runs.
Reference
“The paper's key finding is that using reduced learning rates for proxy model training yields relative performance that strongly correlates with that of fully tuned large-scale LLM pretraining runs.”