Predicting whether an LLM will succeed after processing just a fraction of its prompt can cut token consumption by as much as 44 %. EarlyEval shows that you can halt the evaluation run early without noticeably harming answer quality, turning inference cost from a bottleneck into a manageable budget item.
Before EarlyEval, most efficiency work targeted the number of evaluation tasks rather than the work done within each task. Benchmark‑distillation methods prune whole problems but leave every retained prompt fully processed, so the per‑run token bill stays high.
EarlyEval cuts up to 44.1 % of input tokens and 29.4 % of output tokens while achieving 89–97 % outcome‑prediction accuracy, eliminating 13–26 % of agent steps on average across three benchmarks [1]. The framework trains lightweight LightGBM success and failure classifiers on behavioral, textual, and reference‑solution features; once a classifier crosses a calibrated confidence threshold the run stops, adding only negligible per‑step overhead.
“EarlyEval successfully reproduces the full-run agent rankings with high fidelity across all evaluation targets.” This ranking preservation translates to Spearman ρ ≥ 0.959 on SWE‑bench Verified, TerminalBench and Toolathlon, meaning final answer quality is essentially unchanged despite early termination [1].
The savings come at the cost of a modest drop in per‑agent resolve rates, typically one to two percentage points, and the approach has only been validated on three agentic benchmarks. Extending the technique to more heterogeneous tasks—open‑ended generation, multi‑turn dialogue, or low‑resource languages—remains an open question, as does the sensitivity of the confidence thresholds to model scaling.
Token‑budgeted inference pipelines should replace full‑prompt evaluation with an early‑stopping checkpoint calibrated by LightGBM classifiers. By inserting a cheap prediction layer after a handful of steps, services can reduce token usage by up to about 44 % while keeping leaderboard rankings stable, making large‑scale LLM deployment more financially sustainable.
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