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Paper

DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification

by Shubhashis Roy Dipta arxiv/2605.27858

Free2AITools Nexus Index

38.5

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0

P: Popularity 0

R: Recency 93

Q: Quality 60

Tech Context

Vital Performance —

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 addr...

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Paper Information Summary
Entity Passport
Registry ID	2605.27858
License	arXiv
Provider	arxiv

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{arxiv_2605_27858,
  author = {Shubhashis Roy Dipta},
  title = {DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2605.27858}},
  note = {Accessed via Free2AITools.}
}

APA Style

Shubhashis Roy Dipta. (2026). DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification [Paper]. Free2AITools. https://arxiv.org/abs/2605.27858

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Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0

Popularity (P) 0

Recency (R) 93

Quality (Q) 60

💬 Index Insight

FNI V2.0 for DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification: Authority (A:0), Popularity (P:0), Recency (R:93), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"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 addr..."

❝ Cite Node

@article{Dipta2026DecomposeRL:,
  title={DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification},
  author={Shubhashis Roy Dipta},
  journal={arXiv preprint arXiv:2605.27858},
  year={2026}
}

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Shubhashis Roy Dipta

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Read the full paper on arXiv

📊 Research Signals

📅1970Published

⏱️93RecencyFNI pillar

✅60QualityFNI pillar

🗂️cs.CLField

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🆔 Identity & Source

id: 2605.27858
slug: 2605.27858
source: arxiv
author: Shubhashis Roy Dipta
license: arXiv
tags: arxiv:cs.CL, arxiv:cs.AI, arxiv:cs.LG

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

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