Are Verifier Errors Independent Within a GRPO Group? Evidence from Qwen2.5 Rollouts
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Abstract (arXiv)
Group-based reinforcement learning with verifiable rewards (RLVR) scoresmultiple completions per prompt using automatic verifiers. Analysesbased on independent verifier errors may overlook dependence associatedwith shared answer formats. We investigate this dependence in24,998 groups of eight completions generated by Qwen2.5-1.5B onMATH, GSM8K, and DeepMath-103K. We estimate a pooled within-groupverifier-error correlation of 0.530 (95% confidence interval:0.500--0.560). Under an exchangeable-error model, this correspondsto a design-effect-adjusted effective sample size of 1.70 for aneight-completion group. Dependence varies substantially across answerforms: fractions, radicals, symbolic expressions, and intervals exhibitstronger clustering than unit annotations and percent signs. Replayinggroup-relative advantages across four rule-based verifier configurationsidentifies at least one advantage-sign disagreement in up to 0.83% ofgroups. Because a group is repeated sampling for one prompt, thiswithin-group clustering may reflect shared prompt difficulty as well asshared answer form, and we do not attempt to separate the two here.Unlike studies of correlated judgments across multiple evaluators, ouranalysis examines dependence across completions scored by the sameverifier. These findings motivate prompt- and answer-form-aware analysesof verifier noise rather than characterizations based solely onaggregate error rates.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 15 references that are in the map. First posted on arXiv 6 Sep 2026.
Builds on
11 of 15 references.
References · 11
- 2025Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifierscited by 45
- 2025CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Rewardcited by 40
- 2026Your Group-Relative Advantage Is Biasedcited by 34
- 2026Nine Judges, Two Effective Votes: Correlated Errors Undermine LLM Evaluation Panelscited by 26
- 2025From Accuracy to Robustness: A Study of Rule- and Model-based Verifiers in Mathematical Reasoningcited by 22
- 2025Noise-corrected GRPO: From Noisy Rewards to Unbiased Gradientscited by 13
- 2026CARE: Confounder-Aware Aggregation for Reliable LLM Evaluationcited by 13
- 2026An Imperfect Verifier is Good Enough: Learning with Noisy Rewardscited by 13
- 2026Delay, Plateau, or Collapse: Evaluating the Impact of Systematic Verification Error on RLVRcited by 7
- 2026Gradient Starvation in Binary-Reward GRPO: Why Group-Mean Centering Fails and Why the Simplest Fix Workscited by 6
- 2026Where the Verifier Fails: A Category-Level Audit of Reward Signals in RLVRcited by 3
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Citing papers per year: 2026: 2.
2 papers.
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- 2025Don't Waste Mistakes: Leveraging Negative RL-Groups via Confidence Reweighting
- 2026STAR-GRPO: Canonical Anchoring and Reliability-First Advantages against Representation-Dependent Reward Hacking
- 2026VeriGate: Verifier-Gated Step-Level Supervision for GRPO
- 2025Can GRPO Help LLMs Transcend Their Pretraining Origin?
- 2026On the Hidden Objective Biases of Group-based Reinforcement Learning