Complex claim verification requires decomposing sentences into verifiable subclaims, yet existing methods struggle to align decomposition quality with verification performance. We propose a reinforcement learning (RL) approach that jointly optimizes decomposition quality and verifier alignment using Group Relative Policy Optimization (GRPO). Our method integrates: (i) structured sequential reasoning; (ii) supervised finetuning on teacher-distilled exemplars; and (iii) a multi-objective reward balancing format compliance, verifier alignment, and decomposition quality. Across six evaluation settings, our trained 8B decomposer improves downstream verification performance to (71.75%) macro-F1, outperforming prompt-based approaches ((+1.99), (+6.24)) and existing RL methods ((+5.84)). Human evaluation confirms the high quality of the generated subclaims. Our framework enables smaller language models to achieve state-of-the-art claim verification by jointly optimising for verification accuracy and decomposition quality.

翻译：复杂声明验证需要将句子分解为可验证的子声明，然而现有方法难以将分解质量与验证性能对齐。本文提出一种强化学习方法，通过群体相对策略优化联合优化分解质量与验证器对齐。我们的方法整合了：（i）结构化序列推理；（ii）基于教师蒸馏范例的监督微调；（iii）平衡格式合规性、验证器对齐与分解质量的多目标奖励机制。在六种评估场景中，我们训练的80亿参数分解器将下游验证性能提升至宏观F1值71.75%，显著优于基于提示的方法（提升1.99和6.24个百分点）及现有强化学习方法（提升5.84个百分点）。人工评估证实了生成子声明的高质量特性。本框架通过联合优化验证准确性与分解质量，使小型语言模型能够实现最先进的声明验证性能。