Verifiable Visual Rewards Transfer from Synthetic Scenes to Natural Prompts· 从合成场景到自然提示的可验证视觉奖励转移
Precise instruction following in image generation, such as satisfying object counts and spatial relations, remains an open challenge at least in part because it is learned using unreliable reward models such as object detectors and vision-language models. We introduce Verifiable Visual Rewards (VVR), the first framework for programmatically verifiable image rewards, and show that training on it generalizes to natural prompts. Each VVR task is a scene of geometric objects and relations among them, from which we derive both the prompt and a deterministic verifier, so tasks can be generated in any number and at any chosen complexity. We release VVRBench, with 10,000 tasks over 32 constraint types, and VVRBench-Challenge, with 720 more complex tasks; the strongest model we evaluate---GPT-Image
提出可验证视觉奖励框架，提高图像生成任务对自然提示的泛化能力。
- 核心方法
- 通过引入可验证视觉奖励（VVR）框架，利用几何对象和关系构建场景，生成确定性的验证器，以增强模型对自然提示的泛化能力。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 现有的图像生成方法在满足精确指令（如对象数量和空间关系）时面临挑战，因为它们依赖于不可靠的奖励模型。
- 关键实验
- 实验包括使用VVRBench和VVRBench-Challenge进行模型训练与评估，测试模型在自然提示下的泛化能力。
- 主要贡献
- 发布了VVRBench和VVRBench-Challenge两个数据集，涵盖多种约束类型和复杂任务，改善了现有模型在精确指令跟随上的表现。
- 意义与局限
- 该研究提高了图像生成模型的指令遵循精度和泛化能力，但主要局限于特定的任务类型，可能需要进一步探索更广泛的应用。