Claim-Level Rubric Rewards for Video Caption Reinforcement Learning
This work addresses the reward-design bottleneck in reinforcement learning for dense video captioning, offering a more reliable and fine-grained evaluation method.
CuRe introduces a structured reward framework for dense video captioning that decomposes captions into category-aware atomic claims for fine-grained verification, achieving improved factual accuracy and diversity over holistic or reference-based rewards.
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.