Enhancing reasoning accuracy in large language models during inference time
This paper evaluates three inference-time strategies—self-consistency with temperature/top-p sampling, dual-model cross-verification, and iterative self-reflection—to improve multi-step reasoning in LLMs without parameter updates. The core premise is that aggregating diverse reasoning traces or validating across models yields more reliable outputs than single-pass decoding. The work addresses a practical need for deployment scenarios where retraining is infeasible, though the experimental scope is limited by unclear model specifications and dataset choices.
The paper offers a practical framework for comparing inference-time reliability techniques but suffers from significant methodological vagueness and unsupported quantitative claims. The authors assert "a 9%–15% absolute improvement in accuracy over greedy single-pass decoding" (Abstract), yet Section 4.1 only compares self-consistency with stochastic decoding (64.9% acceptance) against self-consistency with greedy decoding (56.2%)—not against true single-pass greedy. This conflation misleadingly attributes gains solely to sampling strategy rather than aggregation. Furthermore, the employ of a "Mortgage language model" (Jain et al., 2025) for general logical reasoning tasks lacks justification, and critical experimental details are omitted.
The observation that controlled stochastic decoding ($T=0.8$, top-$p=0.9$) improves self-consistency over greedy decoding is methodically documented, with 64.9% acceptance versus 56.2% using greedy aggregation (Section 4.1). The framing of dual-model reasoning as a precision-estimation mechanism rather than an accuracy booster represents a valuable practical insight for high-stakes deployments where ground truth is unavailable. The recognition that self-reflection produces only marginal gains on smaller models (50.6% versus 47.2% baseline) appropriately tempers enthusiasm for that technique in resource-constrained settings.
The experimental design lacks essential transparency and contains questionable dataset choices. The paper describes evaluating on a "Logical Reasoning Improvement Dataset" (Section 2) but reveals it is merely the general-domain Open-Platypus instruction-tuning corpus (Lee et al., 2023), which is not specifically curated for logical reasoning. The models are referred to opaquely as "LLM [1]"—a domain-specific mortgage model (Jain et al., 2025)—yet no rationale is given for using a financial specialist on general reasoning tasks. The dual-model experiments never identify the second model's architecture, size, or relationship to the first. The paper repeatedly references "smaller non-reasoning models" (Abstract, Section 4.3) without disclosing parameter counts or model identities anywhere.
The quantitative evidence partially supports the conclusions but contains logical gaps in comparative analysis. The claimed 9%–15% improvement range conflates the benefits of sampling diversity with self-consistency aggregation itself, as no single-pass greedy baseline is reported. The dual-model approach shows a slight decrease in ground-truth accuracy (48.7% to 47.4%) which the authors reframes as increased precision (Section 4.2), a valid but nuanced interpretation that should not be mistaken for accuracy gains. While the paper cites relevant prior work on self-consistency [5] and self-reflection [4], it fails to position its results against standard reasoning benchmarks (e.g., GSM8K, MATH, StrategyQA) or established inference-time compute scaling laws, limiting external validity.
Reproduction is severely impeded by missing implementation details and opaque model specifications. The paper does not disclose whether the dual-model setup uses identical architectures or distinct models, nor does it identify the "independent verifier model" or "LLM-based judge" used for answer extraction and verification (Section 3.1). No code repository, exact prompt templates, or dataset filtering procedures are provided. The hyperparameters are limited to temperature and top-p values; batch sizes, context windows, and sampling configurations remain unspecified. Without knowledge of the underlying model architectures, parameter counts, or specific checkpoint versions, independent verification of the claimed 64.9% acceptance rates is impossible.
Large Language Models (LLMs) often exhibit strong linguistic abilities while remaining unreliable on multi-step reasoning tasks, particularly when deployed without additional training or fine-tuning. In this work, we study inference-time techniques to improve the reasoning accuracy of LLMs. We systematically evaluate three classes of inference-time strategies: (i) self-consistency via stochastic decoding, where the model is sampled multiple times using controlled temperature and nucleus sampling and the most frequent final answer is selected; (ii) dual-model reasoning agreement, where outputs from two independent models are compared and only consistent reasoning traces are trusted; and (iii) self-reflection, where the model critiques and revises its own reasoning. Across all evaluated methods, we employ Chain-of-Thought (CoT) [1] prompting to elicit explicit intermediate reasoning steps before generating final answers. In this work, we provide a controlled comparative evaluation across three inference-time strategies under identical prompting and verification settings. Our experiments on LLM [2] show that self-consistency with nucleus sampling and controlled temperature value yields the substantial gains, achieving a 9% to 15% absolute improvement in accuracy over greedy single-pass decoding, well-suited for low-risk domains, offering meaningful gains with minimal overhead. The dual-model approach provides additional confirmation for model reasoning steps thus more appropriate for moderate-risk domains, where higher reliability justifies additional compute. Self-reflection offers only marginal improvements, suggesting limited effectiveness for smaller non-reasoning models at inference time.
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