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No Prompt Left Behind: Exploiting Zero-Variance Prompts in LLM Reinforcement Learning via Entropy-Guided Advantage Shaping

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
large language modelsreinforcement learning with verifiable rewardsllm reasoning
Abstract:

Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful framework for improving the reasoning abilities of Large Language Models (LLMs). However, current methods such as GRPO rely only on problems where the model responses to the same input differ in correctness, while ignoring those where all responses receive the same reward—so-called zero-variance prompts. In this work, we argue that such prompts are not useless but can, in fact, provide meaningful feedback for policy optimization. To this end, we introduce RL with Zero-Variance Prompts (RL-ZVP), a novel algorithm that extract learning signals from zero-variance prompts. RL-ZVP directly rewards correctness and penalizes errors even without contrasting responses, modulating feedback with token-level characteristics to preserve informative, nuanced signals. Across six math reasoning benchmarks, RL-ZVP achieves significant improvements of up to 8.61 points in accuracy and 7.77 points in pass rate over GRPO, while consistently outperforming other baselines that filter out zero-variance prompts. These results highlight the untapped potential of learning from zero-variance prompts in RLVR.

Disclaimer
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.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces RL-ZVP, an algorithm that extracts learning signals from zero-variance prompts—inputs where all model responses receive identical rewards. Within the taxonomy, this work occupies the 'Direct Zero-Variance Prompt Utilization' leaf, which currently contains only this paper. This positioning suggests a relatively sparse research direction focused specifically on exploiting rather than filtering uniform-reward prompts. The broader parent category 'Zero-Variance Prompt Exploitation and Advantage Estimation' contains three leaves with four total papers, indicating moderate activity in variance-aware RL methods for LLMs.

The taxonomy reveals neighboring approaches that handle zero-variance data differently. The sibling leaf 'Zero-Variance Elimination and Residual Data Exploitation' contains two papers that filter non-informative prompts, representing an opposing strategy to the original work's utilization approach. Another sibling, 'Adaptive Advantage Estimation for Training Stability', addresses variance issues through advantage computation rather than prompt-level exploitation. Related branches like 'LLM-Guided and Prompt-Informed RL' explore prompt-based reasoning but without explicit zero-variance handling, while 'Meta-Cognition and Self-Alignment' focuses on introspective capabilities rather than variance-specific optimization.

Among the three contributions analyzed across thirty candidate papers, the core RL-ZVP algorithm shows no clear refutation among ten examined candidates, suggesting potential novelty in the direct utilization approach. However, the entropy-guided advantage shaping formula encountered two refutable candidates among ten examined, indicating some overlap with existing advantage estimation techniques. The demonstration that zero-variance prompts provide learning signals also shows no refutation across ten candidates. The limited search scope means these findings reflect top-thirty semantic matches rather than exhaustive coverage of advantage estimation or prompt-based RL literature.

The analysis suggests the work addresses a relatively underexplored niche within prompt-aware RL for LLMs, particularly in its direct exploitation strategy. However, the advantage shaping mechanism appears to have more substantial prior work among the examined candidates. The taxonomy structure indicates this sits at the intersection of variance-aware optimization and prompt-level signal extraction, areas with moderate but not extensive prior exploration based on the eleven total papers across related leaves.

Taxonomy

Core-task Taxonomy Papers
11
3
Claimed Contributions
30
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: Reinforcement learning from zero-variance prompts in large language models. The field structure reflects diverse strategies for aligning and optimizing LLMs through reinforcement learning. The taxonomy organizes work into several major branches: Zero-Variance Prompt Exploitation focuses on leveraging prompt-specific signals and advantage estimation techniques; Multi-Turn and Long-Horizon RL addresses sequential decision-making over extended interactions; Meta-Cognition and Self-Alignment explores models' introspective capabilities for self-improvement; Reward Model Optimization tackles direct alignment methods and the challenges of reward hacking; LLM-Guided and Prompt-Informed RL examines how prompts can steer exploration and policy learning; and RL Infrastructure covers auxiliary tooling and applications. Representative works like Recursive Introspection[1] and Meta-Awareness Reasoning[10] illustrate self-reflective approaches, while Reward Model Overoptimization[3] highlights alignment pitfalls, and WebAgent-R1[4] demonstrates multi-turn agent learning. Particularly active lines of work reveal tensions between exploiting prompt-specific structure versus maintaining generalization across diverse inputs. Methods like Prompt Informed Coverage[8] and Each Prompt Matters[7] emphasize tailoring RL updates to individual prompts, while Adaptive Group Policy[9] and Explore Data Left Behind[2] balance prompt-level signals with broader coverage. Zero-Variance Prompts[0] sits within the Direct Zero-Variance Prompt Utilization cluster, closely aligned with works that directly exploit low-variance prompt characteristics for stable advantage estimation. Compared to PREDILECT[5], which may focus on preference-based learning, or Data-Driven LLM Optimization[11], which emphasizes large-scale data strategies, Zero-Variance Prompts[0] appears to prioritize variance reduction as a core mechanism for improving RL stability and sample efficiency in prompt-conditioned settings.

Claimed Contributions

RL-ZVP algorithm for exploiting zero-variance prompts

The authors propose RL-ZVP, a new reinforcement learning algorithm that extracts useful learning signals from zero-variance prompts (where all sampled responses receive identical rewards) instead of discarding them. The method directly rewards correctness and penalizes errors even without contrasting responses, modulating feedback with token-level characteristics.

10 retrieved papers
Entropy-guided advantage shaping formula

The authors introduce a novel advantage formulation for zero-variance prompts that uses token-level entropy to scale gradient updates. For correct responses, high-entropy tokens receive larger updates; for incorrect responses, high-entropy tokens are penalized less severely to preserve exploration flexibility.

10 retrieved papers
Can Refute
Demonstration that zero-variance prompts provide valuable learning signals

The authors challenge the prevailing practice of discarding zero-variance prompts by demonstrating empirically that these prompts can provide valuable learning signals for policy optimization, achieving significant improvements over methods that filter them out.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

RL-ZVP algorithm for exploiting zero-variance prompts

The authors propose RL-ZVP, a new reinforcement learning algorithm that extracts useful learning signals from zero-variance prompts (where all sampled responses receive identical rewards) instead of discarding them. The method directly rewards correctness and penalizes errors even without contrasting responses, modulating feedback with token-level characteristics.

Contribution

Entropy-guided advantage shaping formula

The authors introduce a novel advantage formulation for zero-variance prompts that uses token-level entropy to scale gradient updates. For correct responses, high-entropy tokens receive larger updates; for incorrect responses, high-entropy tokens are penalized less severely to preserve exploration flexibility.

Contribution

Demonstration that zero-variance prompts provide valuable learning signals

The authors challenge the prevailing practice of discarding zero-variance prompts by demonstrating empirically that these prompts can provide valuable learning signals for policy optimization, achieving significant improvements over methods that filter them out.

Table of Contents

No Prompt Left Behind: Exploiting Zero-Variance Prompts in LLM Reinforcement Learning via Entropy-Guided Advantage Shaping | Novelty Validation