SAM-R1: Leveraging SAM for Reward Feedback in Multimodal Segmentation via Reinforcement Learning

• URL: http://arxiv.org/abs/2505.22596v1
• Date: Wed, 28 May 2025 17:08:28 GMT
• Title: SAM-R1: Leveraging SAM for Reward Feedback in Multimodal Segmentation via Reinforcement Learning
• Authors: Jiaqi Huang, Zunnan Xu, Jun Zhou, Ting Liu, Yicheng Xiao, Mingwen Ou, Bowen Ji, Xiu Li, Kehong Yuan,
• Abstract summary: We propose SAM-R1, a novel framework that enables multimodal large models to perform fine-grained reasoning in image understanding tasks.<n>Our approach is the first to incorporate fine-grained segmentation settings during the training of multimodal reasoning models.<n>With only 3k training samples, SAM-R1 achieves strong performance across multiple benchmarks.
• Score: 26.167394979565454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Leveraging multimodal large models for image segmentation has become a prominent research direction. However, existing approaches typically rely heavily on manually annotated datasets that include explicit reasoning processes, which are costly and time-consuming to produce. Recent advances suggest that reinforcement learning (RL) can endow large models with reasoning capabilities without requiring such reasoning-annotated data. In this paper, we propose SAM-R1, a novel framework that enables multimodal large models to perform fine-grained reasoning in image understanding tasks. Our approach is the first to incorporate fine-grained segmentation settings during the training of multimodal reasoning models. By integrating task-specific, fine-grained rewards with a tailored optimization objective, we further enhance the model's reasoning and segmentation alignment. We also leverage the Segment Anything Model (SAM) as a strong and flexible reward provider to guide the learning process. With only 3k training samples, SAM-R1 achieves strong performance across multiple benchmarks, demonstrating the effectiveness of reinforcement learning in equipping multimodal models with segmentation-oriented reasoning capabilities.

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