Core Insights - The article discusses the integration of reinforcement learning (RL) with Visual-Language-Action (VLA) models to enhance robotic capabilities, enabling robots to understand visual and linguistic instructions while optimizing their actions through trial and error [2][8]. Group 1: VLA and Reinforcement Learning Integration - The combination of VLA models and RL allows robots to interpret tasks and adjust their actions based on feedback, improving their performance in complex environments [2][3]. - The GRAPE framework enhances the generalization of robotic policies by aligning preferences, breaking down complex tasks into manageable stages, and optimizing actions through RL, resulting in a success rate increase of 51.79% for seen tasks and 58.20% for unseen tasks [6][7]. Group 2: Addressing Generalization Challenges - VLA models struggle with generalization in unfamiliar scenarios; however, the VLA-RL framework models the robotic operation as a multi-turn dialogue, achieving higher success rates in 40 complex tasks compared to pure imitation learning [8][10]. - The ReWiND framework generates flexible reward functions through language descriptions, allowing robots to adapt to new tasks with a learning efficiency that is twice as fast in simulations and five times faster in real-world applications [12][14]. Group 3: Fine-Tuning Strategies - The ConRFT framework combines offline and online fine-tuning methods, achieving an average success rate of 96.3% across eight real-world tasks, significantly improving performance compared to traditional supervised learning [15][18]. - The Dual-Actor framework utilizes a pre-trained VLA model to master basic actions before fine-tuning through RL, enhancing the robot's ability to perform complex assembly tasks with higher success rates [20][22]. Group 4: Safety and Efficiency - Safety mechanisms are integrated into RL processes to prevent collisions and damage during robotic exploration, ensuring a secure and efficient learning environment [23][24]. - The article emphasizes the importance of designing efficient multi-modal encoders to address the challenges of integrating visual, linguistic, and action data, which can lead to information loss [27][28].