AlignSAM is a novel framework that aligns the Segment Anything Model (SAM) to open-world contexts using reinforcement learning (RL). The method enables SAM to adapt to diverse downstream tasks without changing its parameters, maintaining its generalization capability. AlignSAM introduces an RL agent to iteratively refine segmentation predictions by interacting with the foundational model. It integrates an RL policy network to generate informative prompts and a semantic recalibration module to provide fine-grained labels for prompts, enhancing the model's ability to handle tasks with explicit and implicit semantics. Experiments on various challenging segmentation tasks demonstrate that AlignSAM outperforms state-of-the-art approaches in terms of efficiency and accuracy. The framework is designed to automatically generate prompts for SAM, allowing it to adapt to different tasks while keeping the backbone network frozen. The RL agent learns to recommend optimal prompting positions, and the semantic recalibration module ensures precise labeling of selected prompts. The method is evaluated on multiple benchmark datasets, showing its effectiveness in tasks such as blur detection, shadow detection, glass detection, and salient object detection. The results indicate that AlignSAM achieves comparable or superior performance to other methods, particularly in scenarios requiring precise segmentation. The framework's ability to handle both explicit and implicit semantics makes it a versatile solution for various segmentation tasks.AlignSAM is a novel framework that aligns the Segment Anything Model (SAM) to open-world contexts using reinforcement learning (RL). The method enables SAM to adapt to diverse downstream tasks without changing its parameters, maintaining its generalization capability. AlignSAM introduces an RL agent to iteratively refine segmentation predictions by interacting with the foundational model. It integrates an RL policy network to generate informative prompts and a semantic recalibration module to provide fine-grained labels for prompts, enhancing the model's ability to handle tasks with explicit and implicit semantics. Experiments on various challenging segmentation tasks demonstrate that AlignSAM outperforms state-of-the-art approaches in terms of efficiency and accuracy. The framework is designed to automatically generate prompts for SAM, allowing it to adapt to different tasks while keeping the backbone network frozen. The RL agent learns to recommend optimal prompting positions, and the semantic recalibration module ensures precise labeling of selected prompts. The method is evaluated on multiple benchmark datasets, showing its effectiveness in tasks such as blur detection, shadow detection, glass detection, and salient object detection. The results indicate that AlignSAM achieves comparable or superior performance to other methods, particularly in scenarios requiring precise segmentation. The framework's ability to handle both explicit and implicit semantics makes it a versatile solution for various segmentation tasks.