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Source note3 min read

AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games

Source

arXiv

arxiv.org/abs/2608.06362v1

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Source

Plain-Language Brief

The paper introduces AV-AIVAT, a tool for evaluating the strength of two agents in imperfect-information games like Heads-Up No-Limit Hold'em (HUNL). AV-AIVAT reduces variance by using conditional mean-zero corrections and combines this with continuously monitored Confidence Sequences (CSs) to ensure that the evaluation stops as soon as there is enough evidence. The tool significantly reduces the number of games needed compared to raw outcomes, improving efficiency and providing a way for third parties to verify the results.

What the Document Says

  • AV-AIVAT is a tool for evaluating agents in imperfect-information games like HUNL.
  • It reduces variance through conditional mean-zero corrections.
  • Combines with continuously monitored Confidence Sequences (CSs).
  • Stops evaluation as soon as there is enough evidence.
  • Reduces the number of games needed compared to raw outcomes by a median factor of 74x for a precision of ±1 Big Blind at a nominal 95% level in HUNL.
  • Exact finite-sample certification uses the Empirical-Bernstein CS (EB-CS), which requires an independently justified bound on corrected payoffs.

Key Concepts Mentioned

  • AV-AIVAT
  • imperfect-information games
  • Conditional mean-zero corrections
  • Confidence Sequences (CSs)
  • evaluation stopping
  • variance reduction
  • HUNL

Claims Made by the Document

  • AV-AIVAT reduces variance in imperfect-information games by a significant factor.
  • AV-AIVAT can stop the evaluation as soon as there is enough evidence.
  • Exact finite-sample certification requires an independently justified bound on corrected payoffs.

Evidence Used

  • Median $54 imes$ variance reduction across 15 LLM agent configurations and 71,439 paired HUNL hands.
  • A median $74 imes$ faster stopping time for AV-AIVAT-corrected outcomes compared to raw outcomes in HUNL at the nominal 95% level and a precision of ±1 Big Blind.
  • Median $1.37 imes$ faster stopping time for HUNL EB-CS runs.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Deciding which of two agents is stronger means playing games until skill outweighs luck, and every game costs money, model inference, or expert time. Since the number of games needed is unknown, fixed-budget evaluations either keep paying after the result is settled or stop before the agents can be told apart, while naive optional stopping with an ordinary confidence interval invalidates the stated level. We make such an evaluation stop as soon as its evidence suffices, with the guarantee intact. The Action-Informed Value Assessment Tool (AIVAT) reduces variance in imperfect-information games through conditional mean-zero corrections, by a median $54\times$ across 15 LLM agent configurations spanning 71,439 paired Heads-Up No-Limit Hold'em (HUNL) hands, but does not say when to stop. We combine AIVAT with continuously monitored Confidence Sequences (CSs) into anytime-valid AIVAT (AV-AIVAT), whose online value model learns only from past games so that no game scores its own correction. At the nominal 95% level and a target precision of $\pm1$ Big Blind, raw outcomes need a median $74\times$ as many hands as AIVAT-corrected outcomes to stop under the Asymptotic CS (AsympCS). Exact finite-sample certification uses the Empirical-Bernstein CS (EB-CS), which needs an independently justified bound on corrected payoffs. We establish such a bound structurally for Leduc hold'em and characterize a width floor set by the CS's bet cap and that bound, which governs how much of a variance gain becomes earlier stopping; the descriptive HUNL EB-CS runs show a median $1.37\times$ stopping-time ratio. AV-AIVAT turns variance reduction into efficient, auditable early stopping while separating asymptotic screening from exact certification, so an evaluation can stop the moment its evidence suffices and hand a third party everything needed to recheck the verdict at that very stopping time.