VR-思維者：通過圖像思維推理增強視頻獎勵模型

VR-Thinker: Boosting Video Reward Models through Thinking-with-Image Reasoning

October 12, 2025

作者: Qunzhong Wang, Jie Liu, Jiajun Liang, Yilei Jiang, Yuanxing Zhang, Jinyuan Chen, Yaozhi Zheng, Xintao Wang, Pengfei Wan, Xiangyu Yue, Jiaheng Liu

cs.AI

摘要

近期，多模态奖励模型（RMs）的进展显著提升了视觉生成模型的训练后效果。然而，现有RMs面临固有局限：（1）视觉输入消耗大量上下文预算，迫使帧数减少，导致细粒度细节丢失；（2）所有视觉信息均压缩至初始提示中，加剧了链式推理过程中的幻觉与遗忘现象。为克服这些问题，我们引入了VideoReward Thinker（VR-Thinker），一种“图像思维”框架，该框架赋予RM视觉推理操作（如选择帧）及可配置的视觉记忆窗口。这使得RM能在上下文限制内主动获取并更新视觉证据，提升推理的准确性与可靠性。我们通过强化微调管道激活视觉推理：（i）冷启动阶段，利用精选的视觉链式思维数据，提炼基础推理技能与操作格式化；（ii）筛选出各维度及整体判断均正确的样本，随后对这些高质量轨迹进行拒绝采样微调，以进一步增强推理能力；（iii）应用群体相对策略优化（GRPO）强化推理。我们的方法在视频偏好基准测试中，尤其是在长视频上，展现了开源模型中的顶尖准确率：7B参数的VR-Thinker在VideoGen Reward上达到80.5%，在GenAI-Bench上为82.3%，在MJ-Bench-Video上为75.6%。这些结果验证了“图像思维”多模态奖励建模的有效性与前景。

English

Recent advancements in multimodal reward models (RMs) have substantially improved post-training for visual generative models. However, current RMs face inherent limitations: (1) visual inputs consume large context budgets, forcing fewer frames and causing loss of fine-grained details; and (2) all visual information is packed into the initial prompt, exacerbating hallucination and forgetting during chain-of-thought reasoning. To overcome these issues, we introduce VideoReward Thinker (VR-Thinker), a thinking-with-image framework that equips the RM with visual reasoning operations (e.g., select frame) and a configurable visual memory window. This allows the RM to actively acquire and update visual evidence within context limits, improving reasoning fidelity and reliability. We activate visual reasoning via a reinforcement fine-tuning pipeline: (i) Cold Start with curated visual chain-of-thought data to distill basic reasoning skills and operation formatting; (ii) select samples whose per-dimension and overall judgments are all correct, then conduct Rejection sampling Fine-Tuning on these high-quality traces to further enhance reasoning; and (iii) apply Group Relative Policy Optimization (GRPO) to strengthen reasoning. Our approach delivers state-of-the-art accuracy among open-source models on video preference benchmarks, especially for longer videos: a 7B VR-Thinker achieves 80.5% on VideoGen Reward, 82.3% on GenAI-Bench, and 75.6% on MJ-Bench-Video. These results validate the effectiveness and promise of thinking-with-image multimodal reward modeling.