VLM-R1：一款穩定且具泛化能力的R1風格大型視覺語言模型

VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

April 10, 2025

作者: Haozhan Shen, Peng Liu, Jingcheng Li, Chunxin Fang, Yibo Ma, Jiajia Liao, Qiaoli Shen, Zilun Zhang, Kangjia Zhao, Qianqian Zhang, Ruochen Xu, Tiancheng Zhao

cs.AI

摘要

近期，DeepSeek R1 展示了强化学习（RL）如何通过一种简单而有效的设计显著提升大型语言模型（LLM）的推理能力。R1 的核心在于其基于规则的奖励机制，该机制利用具有确定性正确答案的任务，实现了精确且稳定的奖励计算。在视觉领域，我们同样观察到，广泛的视觉理解任务天生具备明确的真实标注。这一特性使得它们与基于规则的奖励机制天然兼容。受此启发，我们探索将 R1 风格的强化学习扩展至视觉语言模型（VLM），旨在增强其视觉推理能力。为此，我们开发了 VLM-R1，这是一个专门设计的框架，旨在利用强化学习提升 VLM 在通用视觉语言任务上的表现。通过这一框架，我们进一步探讨了将强化学习应用于视觉领域的可行性。实验结果表明，基于强化学习的模型不仅在视觉理解任务上表现出色，而且在泛化能力上超越了监督微调（SFT）。此外，我们进行了全面的消融研究，揭示了一系列值得注意的发现，包括目标检测中的奖励欺骗现象、“OD 顿悟时刻”的出现、训练数据质量的影响，以及强化学习在不同模型规模下的扩展行为。通过这些分析，我们旨在深化对强化学习如何增强视觉语言模型能力的理解，并希望我们的发现和开源贡献能够支持视觉语言强化学习社区的持续进步。我们的代码和模型可在 https://github.com/om-ai-lab/VLM-R1 获取。

English

Recently DeepSeek R1 has shown that reinforcement learning (RL) can substantially improve the reasoning capabilities of Large Language Models (LLMs) through a simple yet effective design. The core of R1 lies in its rule-based reward formulation, which leverages tasks with deterministic ground-truth answers to enable precise and stable reward computation. In the visual domain, we similarly observe that a wide range of visual understanding tasks are inherently equipped with well-defined ground-truth annotations. This property makes them naturally compatible with rule-based reward mechanisms. Motivated by this observation, we investigate the extension of R1-style reinforcement learning to Vision-Language Models (VLMs), aiming to enhance their visual reasoning capabilities. To this end, we develop VLM-R1, a dedicated framework designed to harness RL for improving VLMs' performance on general vision-language tasks. Using this framework, we further explore the feasibility of applying RL to visual domain. Experimental results indicate that the RL-based model not only delivers competitive performance on visual understanding tasks but also surpasses Supervised Fine-Tuning (SFT) in generalization ability. Furthermore, we conduct comprehensive ablation studies that uncover a series of noteworthy insights, including the presence of reward hacking in object detection, the emergence of the "OD aha moment", the impact of training data quality, and the scaling behavior of RL across different model sizes. Through these analyses, we aim to deepen the understanding of how reinforcement learning enhances the capabilities of vision-language models, and we hope our findings and open-source contributions will support continued progress in the vision-language RL community. Our code and model are available at https://github.com/om-ai-lab/VLM-R1