一文尽览！2025年多篇VLA与RL融合的突破方向

Core Viewpoint - The article discusses a significant revolution in the field of robotic embodiment intelligence, focusing on the integration of Vision-Language-Action (VLA) models with Reinforcement Learning (RL) to address core challenges in real-world robotic decision-making and task execution [2][58]. Summary by Sections GRAPE: Generalizing Robot Policy via Preference Alignment - The GRAPE framework enhances VLA model generalization and adaptability by aligning trajectories, decomposing tasks, and modeling preferences with flexible spatiotemporal constraints [5][6]. - GRAPE shows a 51.79% increase in success rates for seen tasks and a 58.20% increase for unseen tasks, while also reducing collision rates by 37.44% under safety objectives [8][9]. VLA-RL: Towards Masterful and General Robotic Manipulation - The VLA-RL framework addresses the failure of VLA models in out-of-distribution scenarios by utilizing trajectory-level RL expressions and fine-tuning reward models to handle sparse rewards [11][13]. - VLA-RL significantly improves performance on 40 challenging robotic tasks, demonstrating the potential for early reasoning expansion in robotic applications [15]. ReWiND: Language-Guided Rewards Teach Robot Policies without New Demonstrations - The ReWiND framework allows for task adaptation using pre-trained language-based reward functions, eliminating the need for new demonstrations for unseen tasks [18][19]. - ReWiND exhibits a 2.4 times improvement in reward generalization and a 5 times increase in new task adaptation efficiency compared to baseline methods [21]. ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy - ConRFT employs a two-phase reinforcement fine-tuning approach to stabilize VLA model performance during supervised learning [24][26]. - The method achieves a 96.3% success rate across eight practical tasks, improving performance by 144% compared to previous supervised learning methods [29]. RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning - RLDG enhances generalist policies by generating high-quality training data through reinforcement learning, addressing performance and generalization issues [33][34]. - The method shows a 40% increase in success rates for precise operation tasks, demonstrating improved adaptability to new tasks [39]. TGRPO: Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy Optimization - TGRPO introduces online reinforcement learning to VLA models, enhancing robustness and efficiency in policy learning [40][42]. - The method outperforms various baseline approaches in ten operational tasks, validating its effectiveness in improving VLA model adaptability [44]. Improving Vision-Language-Action Model with Online Reinforcement Learning - The iRe-VLAd framework optimizes VLA models through iterative reinforcement and supervised learning, addressing stability and computational challenges [45][47]. - The framework demonstrates effective performance improvements in interactive scenarios, providing a viable path for optimizing large VLA models [51]. Interactive Post-Training for Vision-Language-Action Models - RIPT-VLA offers a scalable, reinforcement learning-based interactive post-training approach to enhance VLA models in low-data environments [52][53]. - The method achieves a 97% success rate with minimal supervision, showcasing its robustness and adaptability across various tasks [57]. Conclusion - The eight studies represent a significant advancement in robotic intelligence, focusing on overcoming industry challenges such as strategy generalization and dynamic environment adaptation, with practical applications in home tasks, industrial assembly, and robotic manipulation [58].