Distill and Align Decomposition for Enhanced Claim Verification
arXiv:2602.21857v1 Announce Type: new 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 verificat
arXiv:2602.21857v1 Announce Type: new 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 quality.
Executive Summary
This article proposes a novel reinforcement learning approach for enhanced claim verification, leveraging Group Relative Policy Optimization (GRPO) to jointly optimize decomposition quality and verifier alignment. The method integrates structured sequential reasoning, supervised fine-tuning, and a multi-objective reward balancing format. The results demonstrate significant improvements in downstream verification performance, outperforming prompt-based approaches and existing RL methods. Human evaluation confirms the high quality of the generated subclaims. This framework enables smaller language models to achieve state-of-the-art claim verification by balancing verification accuracy and decomposition quality. The proposed method has the potential to revolutionize complex claim verification tasks, particularly in the context of artificial intelligence and natural language processing.
Key Points
- ▸ Proposes a novel reinforcement learning approach for enhanced claim verification
- ▸ Integrates structured sequential reasoning, supervised fine-tuning, and multi-objective reward balancing
- ▸ Demonstrates significant improvements in downstream verification performance
Merits
Strength in Joint Optimization
The proposed method jointly optimizes decomposition quality and verifier alignment, achieving a balance between these two critical components of claim verification.
Improved Verification Performance
The results demonstrate significant improvements in downstream verification performance, outperforming existing methods and approaches.
Human Evaluation Confirmation
Human evaluation confirms the high quality of the generated subclaims, providing evidence of the method's effectiveness in producing accurate and reliable results.
Demerits
Limited Evaluation Settings
The article only provides results from six evaluation settings, which may limit the generalizability of the findings to other scenarios or tasks.
Potential Over-Reliance on Large Language Models
The proposed method may rely too heavily on large language models, potentially limiting its applicability to smaller models or resource-constrained environments.
Expert Commentary
The article presents a novel and innovative approach to claim verification, leveraging the strengths of reinforcement learning and optimization techniques. The results are impressive, and the method demonstrates significant improvements in downstream verification performance. However, it is essential to consider the limitations and potential concerns, such as the reliance on large language models and the limited evaluation settings. Nevertheless, this work has the potential to revolutionize claim verification and has significant implications for the development and application of artificial intelligence and natural language processing techniques. Future research should focus on expanding the evaluation settings and exploring the applicability of the proposed method to smaller models and resource-constrained environments.
Recommendations
- ✓ Further research is needed to explore the potential of the proposed method in real-world applications and to address the limitations and concerns raised in this article.
- ✓ Developers and practitioners should consider incorporating the GRPO approach into their claim verification workflows, particularly in scenarios where accuracy and reliability are critical.
