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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 method for evaluating agents in games like poker, which reduces variance by 54 times using AIVAT and continuously monitored Confidence Sequences (CS). The new method stops evaluations as soon as there is enough evidence to determine the stronger agent. At a 95% confidence level and a precision of ±1 Big Blind, AV-AIVAT requires 74 times fewer hands than raw outcomes to stop.

What the Document Says

  • AV-AIVAT is a method that combines AIVAT with continuously monitored Confidence Sequences (CS) for evaluating agents in imperfect-information games like Heads-Up No-Limit Hold'em (HUNL).
  • The method reduces variance by a median factor of 54x across 15 LLM agent configurations.
  • AV-AIVAT stops as soon as there is sufficient evidence to determine the stronger agent, guaranteeing the stated level.
  • At a 95% confidence level and a precision of ±1 Big Blind, AV-AIVAT requires a median 74x fewer hands to stop compared to raw outcomes.
  • Exact finite-sample certification uses the Empirical-Bernstein CS (EB-CS), which requires an independently justified bound on corrected payoffs.
  • The paper establishes such a bound structurally for Leduc hold'em and characterizes a width floor set by the CS's bet cap and that bound.
  • The descriptive HUNL EB-CS runs show a median 1.37x stopping-time ratio.

Key Concepts Mentioned

  • AV-AIVAT
  • AIVAT
  • Confidence Sequences (CS)
  • imperfect-information games
  • Heads-Up No-Limit Hold'em (HUNL)
  • Leduc hold'em
  • variance reduction
  • early stopping

Claims Made by the Document

  • AV-AIVAT reduces variance by a median factor of 54x across 15 LLM agent configurations.
  • At a 95% confidence level and a precision of ±1 Big Blind, AV-AIVAT requires a median 74x fewer hands to stop compared to raw outcomes.
  • The paper establishes a bound structurally for Leduc hold'em.

Evidence Used

  • Median variance reduction of 54x across 15 LLM agent configurations spanning 71,439 paired HUNL hands.
  • Median 74x stopping-time ratio for the descriptive 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.