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GuidedSampling: Steering LLMs Towards Diverse Candidate Solutions at Inference-Time

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Inference-time algorithmsLLMs
Abstract:

Repeated Sampling (RS) is a simple inference-time algorithm that has been shown to improve model performance on complex tasks. Although it is an effective way of scaling inference time, it often struggles to generate diverse solution candidates, frequently relying on the same underlying approach to solve the problem and thus producing redundant samples. To address this limitation, we propose a new inference algorithm, GuidedSampling, which decouples the exploration and generation phases during inference, increasing diversity of generated candidate solutions. The exploration phase identifies multiple concepts that can be utilized to solve the problem, while the generation phase applies a specific concept to provide final solution candidates. We first define the theoretical bounds of GuidedSampling and then empirically demonstrate that it improves the performance of base model at pass@50 by on an average ∼21.6\sim21.6% across various benchmarks compared to RS. Furthermore, models trained on trajectories of GuidedSampling exhibit substantial performance improvements at pass@5 by on an average ∼9.7\sim9.7%, compared to models trained on traditional RS. Additionally, models trained with GuidedSampling increases the average number of concepts per instance (1.67→3.031.67 \to 3.03), yielding a diverse set of candidates than traditional RS.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes GuidedSampling, an inference-time algorithm that decouples exploration and generation phases to improve diversity of LLM solution candidates. It resides in the Concept-Guided and Multi-Phase Generation leaf, which contains only three papers total. This is a notably sparse research direction within the broader taxonomy of fifty papers, suggesting that explicit multi-phase conceptual scaffolding for diversity remains relatively underexplored compared to single-pass stochastic methods or quality-diversity frameworks.

The taxonomy reveals that most diversity-oriented work clusters around adaptive sampling parameters, prompt-level variation, or tree-based search structures. GuidedSampling's nearest conceptual neighbors include Flow of Reasoning and other multi-step approaches that interleave planning with generation, contrasting sharply with entropy-based temperature tuning or beam search variants. The scope note for this leaf explicitly excludes single-phase generation, positioning the work at the intersection of structured exploration and conceptual guidance rather than purely stochastic diversification.

Among twenty candidates examined, the core GuidedSampling algorithm shows one refutable match out of ten candidates reviewed, while the post-training method using GuidedSampling trajectories found no refutations across ten candidates. The theoretical bounds contribution was not evaluated against prior work. This limited search scope suggests that within the examined semantic neighborhood, the multi-phase conceptual approach appears relatively novel, though the analysis does not cover the full breadth of inference-time scaling or quality-diversity literature.

Based on top-twenty semantic matches and the sparse taxonomy leaf, the work appears to occupy a less-crowded niche. However, the search scope is narrow, and the single refutation for the core algorithm indicates some overlap with existing multi-phase or concept-driven methods. A more exhaustive review would be needed to assess whether the specific decoupling mechanism and theoretical formalization represent substantive advances over related structured exploration techniques.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
20
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Improving diversity of LLM solution candidates through guided inference-time sampling. The field addresses how to generate varied yet high-quality outputs from large language models without additional training. The taxonomy reveals several complementary directions: Diversity-Oriented Sampling Strategies focus on stochastic mechanisms and temperature tuning (e.g., Entropy Dynamic Temperature[3], Locally Typical Sampling[7]); Quality-Diversity Trade-off Optimization balances exploration with correctness, often drawing on evolutionary computation ideas (Quality-Diversity Algorithms[18]); Structured Search and Exploration Methods employ tree-based or beam-based techniques (Diverse Beam Search[39], Inference-Time Tree Search[40]); and Concept-Guided and Multi-Phase Generation orchestrates sampling through explicit reasoning steps or conceptual scaffolding. Meanwhile, Training-Free Inference-Time Scaling and Model Collaboration branches explore how to amplify performance by combining multiple samples or models, and Theoretical Foundations provide formal decoding frameworks (Decoding Strategies Survey[24], Informational Interpretations[10]). Recent work highlights tensions between pure stochasticity and guided control. Some methods pursue diversity via adaptive temperature schedules or entropy-based adjustments (Adaptive Temperature[26], Control Temperature[31]), while others inject external rewards or verifiers to steer generation toward valid solutions (Reward-Augmented Decoding[32], Execution Guided Generation[9]). GuidedSampling[0] sits within the Concept-Guided and Multi-Phase Generation branch, emphasizing structured, multi-step processes that interleave conceptual planning with sampling. This contrasts with single-pass stochastic approaches like Diversified Sampling[2] and aligns more closely with Flow of Reasoning[33], which also decomposes generation into interpretable phases. Compared to Quality-Diversity Algorithms[18] that optimize explicit diversity metrics, GuidedSampling[0] leverages intermediate conceptual anchors to naturally diversify candidates while maintaining coherence, illustrating an emerging theme of marrying symbolic guidance with neural sampling.

Claimed Contributions

GuidedSampling inference-time algorithm

The authors introduce GuidedSampling, an inference-time algorithm that separates the exploration of diverse concepts (theorems or ideas) from the generation of final solutions. This decoupling enables explicit control over exploration and increases the diversity of candidate solutions compared to traditional repeated sampling.

10 retrieved papers
Can Refute
Theoretical bounds for GuidedSampling

The paper establishes formal theoretical bounds characterizing when GuidedSampling outperforms repeated sampling. The analysis includes conditions on concept relevance probability and amplification factors that determine the algorithm's effectiveness.

0 retrieved papers
Post-training method using GuidedSampling trajectories

The authors demonstrate that fine-tuning language models on synthetic data generated via GuidedSampling trajectories substantially improves performance. They introduce two training settings (Final-Answer Only and Concept-Augmented Answer) that leverage the exploration-aware data for post-training.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

GuidedSampling inference-time algorithm

The authors introduce GuidedSampling, an inference-time algorithm that separates the exploration of diverse concepts (theorems or ideas) from the generation of final solutions. This decoupling enables explicit control over exploration and increases the diversity of candidate solutions compared to traditional repeated sampling.

Contribution

Theoretical bounds for GuidedSampling

The paper establishes formal theoretical bounds characterizing when GuidedSampling outperforms repeated sampling. The analysis includes conditions on concept relevance probability and amplification factors that determine the algorithm's effectiveness.

Contribution

Post-training method using GuidedSampling trajectories

The authors demonstrate that fine-tuning language models on synthetic data generated via GuidedSampling trajectories substantially improves performance. They introduce two training settings (Final-Answer Only and Concept-Augmented Answer) that leverage the exploration-aware data for post-training.

Table of Contents