DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification

• URL: http://arxiv.org/abs/2605.27858v1
• Date: Wed, 27 May 2026 02:19:52 GMT
• Title: DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification
• Abstract summary: We propose DecomposeRL as an accurate claim-verifier that produce inspectable traces.<n>DecomposeRL frames decomposition as an RL policy trained with GRPO.<n>We show that a DecomposeRL-7B policy trained with full supervision on only 5K curated claims achieves 86.3 in-domain and 69.8 out-of-domain balanced accuracy.
• Score: 7.917135080399156
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Claim verification splits between end-to-end classifiers that are accurate but yields no inspectable traces, and decomposition-based methods produce inspectable traces but lag performance on benchmark datasets. We propose DecomposeRL an accurate claim-verifier that produce inspectable traces. DecomposeRL frames decomposition as an RL policy trained with GRPO and a multi-faceted reward ensemble, enabling both fully supervised and semi-supervised learning from unlabeled claims. DecomposeRL addresses the prohibitive training cost of GRPO with a data-curation funnel that distills 115K fact-verification claims into a compact, learning-signal-dense subset of 5K claims. We show that a DecomposeRL-7B policy trained with full supervision on only ~5K curated claims achieves 86.3 in-domain and 69.8 out-of-domain balanced accuracy across 11 claim-verification benchmarks containing biomedical, political, scientific, and general-domain claims. Despite being 4x smaller, it matches 32B baselines and GPT-4.1-mini, and it further outperforms baselines in a semi-supervised setting with only 10% labeled claims data. Code, data, and models are available at https://dipta007.github.io/DecomposeRL

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