多模态 RLVR 是当前强化学习与视觉语言结合的热点方向，这篇教程从数据集到奖励函数再到导出一步到位，做多模态推理或 RL 研究的团队可以直接照着搭，省去自己踩坑的时间。
本文是一篇技术教程，详细介绍了如何使用 TuringEnterprises/Open-MM-RL 数据集构建完整的多模态强化学习与可验证奖励（RLVR）管线。教程涵盖数据集加载、模式检查、领域分析、问题长度与答案类型统计、图像分布可视化等预处理步骤。还构建了轻量级奖励函数，支持精确匹配与语义评分，并演示了 GRPO 导出流程。该管线为多模态推理任务提供了可复现的实践框架，适合研究者和开发者快速上手。
Design a Complete Multimodal RLVR Pipeline with Open-MM-RL, Vision-Language Prompting, Reward Scoring, and GRPO Export
In this tutorial, we explore the TuringEnterprises/Open-MM-RL dataset as a practical foundation for multimodal reasoning and reinforcement learning with verifiable rewards. We load the dataset, inspect its schema, analyze domains, formats, question lengths, answer types, and image distributions, and visualize representative examples from each domain. We also build a lightweight reward function that checks exact, […] The post Design a Complete Multimodal RLVR Pipeline with Open-MM-RL, Vision-Language Prompting, Reward Scoring, and GRPO Export appeared first on MarkTechPost .