让AI回答时更聚焦图像证据，提升视觉推理准确性
Attend to Evidence: Evidence-Anchored Spatial Attention Supervision for Multimodal RLVR
- 用标注的图像区域生成注意力目标，指导模型关注正确视觉证据
- 在感知、数学和多模态推理任务上比基线提升2.5至3.1分
- 适合需要精准视觉定位的问答系统开发者
基于可验证奖励的强化学习（RLVR）通过优化最终答案对应的奖励来提升视觉语言模型（VLMs），但仅依赖结果奖励无法告诉模型哪些图像区域支撑了答案。对于需要视觉定位的问题，这类奖励难以区分由视觉证据支持的回答与依赖语言先验或侥幸猜中的回答。本文提出EASE（Evidence-Anchored Spatial Attention），在多模态RLVR中引入视觉证据过程监督。EASE将标注的证据区域转换为平滑的视觉标记目标，并仅在高奖励轨迹上引导模型响应-图像注意力对齐，且仅训练阶段使用标注信息，推理时仍仅需原始图像与问题。在Qwen2.5-VL-7B、Qwen3-VL-4B和Qwen3-VL-8B上，EASE相比DAPO平均提升2.5至3.1分，涵盖感知、幻觉、视觉数学和多模态推理等基准测试。诊断与消融实验表明，EASE显著提升了视觉注意力与标注证据区域的一致性。
原文摘要 · Abstract (English)
Reinforcement learning with verifiable rewards (RLVR) improves vision-language models (VLMs) by optimizing outcome rewards derived from final answers. However, such outcome-only rewards do not tell the model which image regions justify an answer. For questions that require visual grounding, these rewards cannot distinguish responses supported by relevant visual evidence from those produced by language-prior shortcuts or lucky guesses. We introduce EASE (Evidence-Anchored Spatial Attention), which augments multimodal RLVR with visual-evidence process supervision. EASE converts annotated evidence regions into a smoothed visual-token target and uses it to guide response-to-image attention during RL training, but only on high-reward trajectories. The annotations are used solely as privileged training labels, while inference requires only the original image and question. Across Qwen2.5-VL-7B, Qwen3-VL-4B, and Qwen3-VL-8B, EASE raises average scores over DAPO by 2.5 to 3.1 points on perception, hallucination, visual math, and multimodal reasoning benchmarks. Diagnostics and ablations show that EASE better aligns visual attention with annotated evidence regions.
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