AI BriefingPACE: A Proxy for Evaluating Agent Capabilities
Key point
The PACE framework accurately predicts performance on costly LLM agent benchmarks using only a small amount of data.
Details
The PACE framework proposes a method that uses only a small number of atomic evaluation instances to predict performance on costly LLM agent benchmarks.
This method trains a regression model on instances selected from non-agentic benchmarks to predict agent benchmark scores. Through this, it achieves a 99%+ reduction in evaluation cost while maintaining high accuracy with a mean absolute error (MAE) of under 4%.
This technique can be used as an efficient evaluation tool in the model development and selection process.
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