EarlyEval: Cheaper Agent Evaluation via Early Outcome Prediction
Read the original on arXiv Computation and Language →EarlyEval introduces a lightweight framework that predicts an LLM agent’s final outcome early in its execution, allowing the run to halt when a LightGBM classifier reaches a calibrated confidence threshold. By training success and failure classifiers on behavioral, textual, and reference-solution features, EarlyEval can cut 13%-26% of agent steps and up to 44.1% of input tokens while maintaining 89%-97% prediction accuracy. Across three benchmarks—SWE-bench Verified, TerminalBench, and Toolathlon—this approach reduces evaluation costs with minimal impact on per-agent resolve rates.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.