@inproceedings{magomere-etal-2026-distill,
    title = "Distill and Align Decomposition for Enhanced Claim Verification",
    author = "Magomere, Jabez  and
      Kochkina, Elena  and
      Mensah, Samuel  and
      Kaur, Simerjot  and
      Acero, Fernando  and
      Oncevay, Arturo  and
      Smiley, Charese  and
      Liu, Xiaomo  and
      Veloso, Manuela",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.309/",
    pages = "5887--5912",
    ISBN = "979-8-89176-386-9",
    abstract = "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."
}