Test-time Verification via Optimal Transport: Coverage, ROC, & Sub-optimality

ICLR 2026 Conference SubmissionAnonymous Authors
Test-time verificationCoverageApproximate verifierROC
Abstract:

While test-time scaling with verification has shown promise in improving the performance of large language models (LLMs), role of the verifier and its imperfections remain underexplored. The effect of verification manifests through interactions of three quantities: (i) the generator’s coverage, (ii) the verifier’s region of convergence (ROC), and (iii) the sampling algorithm’s sub-optimality. Though recent studies capture subsets of these factors, a unified framework quantifying the geometry of their interplay is missing. We frame verifiable test-time scaling as a transport problem. This characterizes the interaction of coverage, ROC, and sub-optimality, and uncovers that the sub-optimality–coverage curve exhibits three regimes. A transport regime -- where sub-optimality increases with coverage, a policy improvement regime -- where sub-optimality may decrease with coverage, depending on the verifier’s ROC, and a saturation regime -- where sub-optimality plateaus, unaffected by coverage. We further propose and analyze two classes of sampling algorithms -- sequential and batched, and examine how their computational complexities shape these trade-offs. Empirical results with \texttt{Qwen}, \texttt{Llama}, and \texttt{Gemma} models corroborate our theoretical findings.

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Overview

Overall Novelty Assessment

The paper contributes a transport-theoretic framework for analyzing test-time verification, characterizing the interplay between generator coverage, verifier region of convergence, and sampling sub-optimality. It resides in the 'Geometric Characterization of Verifier Performance' leaf under 'Theoretical Foundations of Imperfect Verification', which contains only two papers total. This represents a sparse research direction within the broader taxonomy of sixteen papers, suggesting the geometric perspective on verifier behavior is relatively underexplored compared to algorithmic or empirical approaches in sibling branches.

The taxonomy reveals neighboring work primarily in algorithmic domains: 'Test-Time Scaling Algorithms' contains sampling-based alignment and search methods, while 'Verifier Design and Improvement' focuses on ensemble frameworks and uncertainty quantification. The sibling leaf 'Fundamental Limits and Scaling Laws' examines theoretical bounds on inference scaling with imperfect verifiers, sharing the theoretical orientation but differing in analytical approach. The scope note for the paper's leaf explicitly excludes sampling algorithm design and ensemble methods, positioning this work as foundational theory rather than practical system development, though the proposed sequential and batched algorithms bridge toward the algorithmic branches.

Among twenty-six candidates examined across three contributions, none yielded clear refutations. The optimal transport framework examined six candidates with zero refutable matches; the three-regime characterization examined ten candidates with zero refutations; the sampling algorithms examined ten candidates, also with zero refutations. This suggests that within the limited search scope, the geometric transport perspective and the specific regime decomposition appear distinct from prior work. The absence of overlapping contributions across all three claims indicates the framework's conceptual approach may diverge from existing theoretical analyses, though the search scale limits definitive conclusions about field-wide novelty.

Based on examination of thirty candidates from semantic search, the work appears to occupy a relatively unexplored theoretical niche within test-time verification research. The sparse population of its taxonomy leaf and the lack of refutable prior work among examined candidates suggest conceptual distinctiveness, though the limited search scope means potentially relevant work outside top-ranked semantic matches remains unexamined. The geometric framing and transport-theoretic analysis represent a methodological departure from the predominantly algorithmic focus of neighboring research directions.

Taxonomy

Core-task Taxonomy Papers
16
3
Claimed Contributions
26
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Test-time verification with imperfect verifiers in large language models. The field addresses how to leverage verifiers—models that assess solution quality—during inference, even when these verifiers themselves make errors. The taxonomy reveals several complementary research directions. Theoretical Foundations of Imperfect Verification explores the mathematical underpinnings of verifier behavior, including geometric characterizations and performance limits. Test-Time Scaling Algorithms develops practical methods for using verifiers to guide search or sampling at inference time, with works like Sample Don't Search[6] and ROC Reroll[7] proposing efficient strategies. Verifier Design and Improvement focuses on building better verification models through architectural choices and training techniques, as seen in Taming Imperfect Verifiers[12] and Shrinking Generation Verification Gap[9]. Training with Synthetic Verification examines how imperfect verifiers can be used during model training itself, while Domain-Specific Applications tailors verification approaches to particular problem settings such as recommendation systems or formal correctness. A central tension across these branches concerns the trade-off between verifier accuracy and computational cost during test-time scaling. Works like Inference Scaling Flaws[8] highlight fundamental limitations when verifiers are unreliable, while Verification Limits Training[5] and Solve Detect Verify[10] explore how to mitigate these issues through careful design. The original paper[0] sits within the theoretical branch, specifically examining geometric characterizations of verifier performance—a perspective that complements the more algorithmic focus of neighboring works. Where Verification Dynamics[4] might study temporal or iterative aspects of verification processes, the geometric approach provides a spatial or structural lens on how verifier errors manifest and propagate. This theoretical grounding offers insights that inform both the design of better verifiers and the development of more robust test-time algorithms across the taxonomy.

Claimed Contributions

Optimal transport framework for test-time verification

The authors formulate test-time verification as an optimal transport problem where the goal is to transport the proposal distribution of a reference LLM to a target distribution defined by a ground-truth verifier. This framework enables exact analysis of how generator coverage, verifier region of convergence, and sampling sub-optimality interact.

6 retrieved papers
Three-regime characterization of sub-optimality versus coverage

The authors discover and characterize three distinct regimes in the relationship between sub-optimality and coverage: a transport regime dominated by optimal transport cost, a policy improvement regime where verifier accuracy enables sub-optimality reduction, and a saturation regime where sub-optimality plateaus regardless of coverage.

10 retrieved papers
Sequential and batched sampling algorithms with theoretical guarantees

The authors propose sequential rejection sampling and sequential maximal coupling algorithms for the sequential protocol, and analyze batched variants. They derive exact sub-optimality bounds and computational complexity characterizations for these algorithms, revealing when rejection sampling versus best-of-N approaches are preferable.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Optimal transport framework for test-time verification

The authors formulate test-time verification as an optimal transport problem where the goal is to transport the proposal distribution of a reference LLM to a target distribution defined by a ground-truth verifier. This framework enables exact analysis of how generator coverage, verifier region of convergence, and sampling sub-optimality interact.

Contribution

Three-regime characterization of sub-optimality versus coverage

The authors discover and characterize three distinct regimes in the relationship between sub-optimality and coverage: a transport regime dominated by optimal transport cost, a policy improvement regime where verifier accuracy enables sub-optimality reduction, and a saturation regime where sub-optimality plateaus regardless of coverage.

Contribution

Sequential and batched sampling algorithms with theoretical guarantees

The authors propose sequential rejection sampling and sequential maximal coupling algorithms for the sequential protocol, and analyze batched variants. They derive exact sub-optimality bounds and computational complexity characterizations for these algorithms, revealing when rejection sampling versus best-of-N approaches are preferable.

Test-time Verification via Optimal Transport: Coverage, ROC, & Sub-optimality | Novelty Validation