今年大概率产了n篇VLA+RL工作吧？！

Core Insights - The article emphasizes the importance of Reinforcement Learning (RL) in enhancing the generalization capabilities of Vision-Language-Action (VLA) models, with some experiments showing performance improvements of up to 42.6% on out-of-distribution tasks [2]. Group 1: VLA and RL Integration - VLA models are currently reliant on RL to overcome limitations in real-world out-of-distribution scenarios, where imitation learning alone proves insufficient [2]. - Recent advancements in VLA+RL frameworks have led to significant breakthroughs, with several notable papers published this year [2]. - Tools supporting VLA+RL frameworks, such as Rlinf, are becoming increasingly comprehensive, offering a variety of methods for researchers [2]. Group 2: Notable Research Papers - A summary of representative VLA+RL research papers from the past two years is provided, indicating a growing body of work in this area [5]. - Specific papers mentioned include "NORA-1.5," "Balancing Signal and Variance," and "CO-RFT," which focus on different aspects of VLA and RL integration [5][10]. - The article encourages further research in these areas and offers assistance for those looking to explore VLA, real2sim2real, and RL [3].