今年大概率产了n篇VLA+RL工作吧？！

Core Insights - The article emphasizes the integration of Reinforcement Learning (RL) with Vision-Language-Action (VLA) models to enhance their generalization capabilities, particularly in out-of-distribution (OOD) scenarios, where performance improvements can reach up to 42.6% [2]. Group 1: Research Directions - The article suggests that future research should focus on the combination of VLA and RL, encouraging collaboration with research assistants for guidance on starting projects in these areas [3]. - Several notable recent works in VLA+RL have been highlighted, showcasing significant advancements in the field [5][10]. Group 2: Notable Papers and Projects - A list of representative papers from the last two years is provided, including titles such as "NORA-1.5" and "Balancing Signal and Variance," which focus on various aspects of VLA and RL integration [5][10]. - Links to project homepages and paper PDFs are shared for further exploration of these works [6][9][12]. Group 3: Tools and Frameworks - The article mentions the development of tools like Rlinf, which supports a growing number of methods for VLA+RL frameworks, indicating a trend towards more robust and versatile research tools [2][11].