CFR without Unbiasedness: Deterministic Guarantees for Persistent Public-Chance Schedules

[2608.14761] CFR without Unbiasedness: Deterministic Guarantees for Persistent Public-Chance Schedules Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Science and Game Theory arXiv:2608.14761 (cs) [Submitted on 14 Aug 2026] Title: CFR without Unbiasedness: Deterministic Guarantees for Persistent Public-Chance Schedules Authors: Jiaxing Guo , Lei Ye View a PDF of the paper titled CFR without Unbiasedness: Deterministic Guarantees for Persistent Public-Chance Schedules, by Jiaxing Guo and 1 other authors View PDF HTML (experimental) Abstract: At a finite public-chance cut, counterfactual regret minimization (CFR) must choose how many outcomes to evaluate before each regret update. Exact evaluation processes the full cut at one strategy profile; persistent partial evaluation processes a fixed without-replacement order across evolving profiles. The latter covers every outcome once per epoch, yet its feedback is generally conditionally biased because earlier batches influence the profiles seen by later batches. We establish a deterministic target-transfer theorem for uniform, nonnested additive public cuts. The theorem bounds full-cut exploitability by regret on the delivered feedback and a public-debit term that couples prefix coverage discrepancy with motion along the realized strategy path. Consecutively balanced schedules consequently converge for additive signed regret matching (RM) and RM+ under predetermined averaging weights, while a fixed RM+ construction proves that the discrepancy--path product is necessary in general. A component-resolved form of the theorem converts an execution trace into a numerical exploitability certificate. On two released heads-up no-limit hold'em turn endgames, persistent order improves substantially over fresh reshuffling despite identical epochwise coverage, and partial coverage wins every registered shallow matched-budget comparison. A depth study locates a crossover between 32 and 64 full-cut outcome budgets, after which complete coverage dominates. These results characterize public-chance width and order as learning variables and provide a deterministic basis for designing and auditing persistent CFR schedules. Comments: 27 pages, 2 figures Subjects: Computer Science and Game Theory (cs.GT) ; Machine Learning (cs.LG) Cite as: arXiv:2608.14761 [cs.GT] (or arXiv:2608.14761v1 [cs.GT] for this version) https://doi.org/10.48550/arXiv.2608.14761 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jiaxing Guo [ view email ] [v1] Fri, 14 Aug 2026 09:28:35 UTC (74 KB) Full-text links: Access Paper: View a PDF of the paper titled CFR without Unbiasedness: Deterministic Guarantees for Persistent Public-Chance Schedules, by Jiaxing Guo and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.GT < prev | next > new | recent | 2026-08 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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