今年的VLA+RL的工作正在排队等着录用......

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 are evolving, with recommendations for resources like Rlinf, which offers a growing number of supported methods [2]. Group 2: Notable Research Papers - A summary of representative VLA+RL research papers from the past two years is provided, highlighting their contributions to the field [5]. - Key papers include "NORA-1.5," which focuses on a VLA model trained using world model and action-based preference rewards, and "Balancing Signal and Variance," which discusses adaptive offline RL post-training for VLA flow models [5][10]. - Other significant works include "ReinboT," which enhances robot visual-language manipulation through RL, and "WMPO," which optimizes policies based on world models for VLA [8][10]. Group 3: Future Research Directions - The article suggests that future research should align with the advancements in VLA and RL, encouraging collaboration and consultation for those interested in exploring these areas [3].