Adaptive framework uses sequential testing to reduce model evaluation costs

The article proposes replacing rigid fixed-size benchmarks with an adaptive evaluation framework based on sequential testing to address the inefficiency of current model evaluation methods.

• The framework combines sequential testing with stopping criteria tailored for diminishing returns detection and minimum detectable effect size.
• It allows for varying levels of statistical power needed for different objectives like model ranking or selection.
• Demonstrations on the Open VLM Leaderboard show an 80% reduction in computational cost compared to fixed-size evaluation while maintaining statistical significance.

This approach provides a principled way to navigate the trade-off between efficiency and reliability, preventing excessive costs or compromised reliability.