DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification
arXiv:2605.27858v1 Announce Type: cross 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 da
The increasing demand for explainable and traceable AI, particularly in high-stakes applications, is driving innovation in methods that combine performance with interpretability.
This development addresses a critical trade-off in AI, offering a path for more reliable and auditable AI systems, which is crucial for broad adoption in sensitive domains.
AI systems can now achieve high performance in tasks like claim verification while simultaneously providing inspectable reasoning traces, moving beyond black-box classification.
- · AI developers
- · Auditors and regulators
- · Industries requiring explainable AI
- · End-to-end black-box classifiers
- · Systems focused solely on performance without transparency
Improved trust and adoption of AI in critical decision-making processes.
New standards and regulations may emerge requiring traceable AI for specific applications.
The development of 'AI agents' could be significantly accelerated by the ability to audit and understand their decision-making paths.
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Read at arXiv cs.LG