AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games
Score
76首次将方差削减与随时有效停止结合，给出了可认证的 Agent 评估停止规则，增量显著。
包含理论保证、大规模 HUNL 实证与可复现审计流程，内容扎实完整。
直接降低模型评估的资金和专家时间成本，对 LLM Agent 选型与迭代有直接帮助。
在 LLM Agent 评估需求上升期推出，随时有效推断方法切中当前痛点。
来源为 arXiv AI/CL/LG 分类预印本，可复现性较好，但未经正式同行评议。
Base 84 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
Novel method, large-scale experiments, and direct cost savings, though the realized stopping-time gain is limited in some settings; overall score is solid.
AI commentary
「What I appreciate most here is that the paper does not stop at theory: over 70k HUNL hands, it turns saved samples into earlier decisions. For teams that compare dozens of agent versions daily, this can visibly compress evaluation cycles; for online-statistics researchers, the structural bound for EB-CS offers a useful framework.」
Quick overview
Determining which of two agents is stronger requires playing games until evidence outweighs luck, and every game costs money, inference, or expert time. Fixed-budget evaluations either waste resources after the result is settled or stop too early to distinguish agents, while naive optional stopping with ordinary confidence intervals invalidates the guarantee. AV-AIVAT combines AIVAT's variance reduction with continuously monitored confidence sequences to deliver certified anytime-valid stopping in imperfect-information games. Across 15 LLM agent configurations spanning 71,439 paired Heads-Up No-Limit Hold'em hands, AIVAT reduces variance by a median 54x; at nominal 95% level and ±1 Big Blind precision, raw outcomes need a median 74x as many hands to stop. The Empirical-Bernstein CS provides exact finite-sample certification with a structurally justified bound on corrected payoffs, making the evaluation auditable and reproducible at the stopping time.
- AIVAT cuts variance by a median 54x across HUNL hands but provides no stopping rule.
- AV-AIVAT pairs AIVAT with anytime-valid confidence sequences, reducing required hands to about 1/74 of raw evaluation at 95% confidence.
- EB-CS gives exact finite-sample certification, with a structural payoff bound established for Leduc hold'em.
- Evaluations can stop as soon as evidence suffices, leaving an auditable and reproducible record.
Full text
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