@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Distill and Align Decomposition for Enhanced Claim Verification
%A Magomere, Jabez
%A Kochkina, Elena
%A Mensah, Samuel
%A Kaur, Simerjot
%A Acero, Fernando
%A Oncevay, Arturo
%A Smiley, Charese
%A Liu, Xiaomo
%A Veloso, Manuela
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F magomere-etal-2026-distill
%X 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.
%U https://aclanthology.org/2026.findings-eacl.309/
%P 5887-5912
Markdown (Informal)
[Distill and Align Decomposition for Enhanced Claim Verification](https://aclanthology.org/2026.findings-eacl.309/) (Magomere et al., Findings 2026)
ACL
- Jabez Magomere, Elena Kochkina, Samuel Mensah, Simerjot Kaur, Fernando Acero, Arturo Oncevay, Charese Smiley, Xiaomo Liu, and Manuela Veloso. 2026. Distill and Align Decomposition for Enhanced Claim Verification. In Findings of the Association for Computational Linguistics: EACL 2026, pages 5887–5912, Rabat, Morocco. Association for Computational Linguistics.