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arXiv · 2607.05150

Claim-Level Rubric Rewards for Video Caption Reinforcement Learning

Mingqi Gao·Hongyuan Dong·Yifei Chen·Zhisheng Zhong·Zheng Ruan·Wenjin Hou·Yu Chen·Han Hu·Yansong Tang
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Abstract

In this paper, we introduce Claim-Level Rubric Rewards (CuRe), a structured reward framework designed to address the reward-design bottleneck in reinforcement learning for dense video captioning. Existing reward designs generally fall into two categories: holistic response-level judgment across heterogeneous criteria, or alignment-based evaluation against reference captions. However, both paradigms suffer from fundamental limitations. Holistic rewards struggle to ensure factual accuracy and are prone to stylistic reward hacking, while reference-based rewards overly rely on rigid textual alignment, failing to preserve the completeness and diversity inherent to open-ended generation tasks. To address these challenges, CuRe reformulates reward modeling as fine-grained claim-level verification. Specifically, CuRe decomposes captions into category-aware atomic claims through a structured rubric, converting holistic evaluation into simpler and more reliable claim-level verification.

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Mingqi Gao, Hongyuan Dong, Yifei Chen, Zhisheng Zhong, Zheng Ruan, Wenjin Hou, Yu Chen, Han Hu, Yansong Tang. 2026-07-06. Claim-Level Rubric Rewards for Video Caption Reinforcement Learning. https://arxiv.org/abs/2607.05150

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