Core Insights - The article discusses the shift in research trends towards incorporating Reinforcement Learning (RL) in Visual Language Models (VLA), moving beyond Supervised Fine-Tuning (SFT) to enhance model performance and adaptability [1][2]. Group 1: RL Methodologies - Various RL methodologies are categorized, including online RL, offline RL, iterative RL, and inference-time improvement, but the author emphasizes that the effectiveness of these methods is more important than their classification [1]. - The real-world applicability of RL is crucial, with safety and efficiency being key concerns during data collection and model deployment [2]. Group 2: Task Performance and Challenges - Current RL implementations show promising results in single-task performance, with examples like Pi-star-0.6 requiring around 1,000 trajectories for complex tasks such as folding clothes [3]. - A significant challenge remains in enabling RL to handle multiple tasks effectively, ensuring that tasks can positively influence each other rather than detract from overall performance [3]. Group 3: Reward Functions and Research Directions - The necessity of learning reward functions or value functions is debated, with the potential for reduced variance in optimization being a key benefit, although this need may diminish as pre-trained VLA models improve [4][5]. - Research directions are identified, focusing on issues related to sparse rewards, the scale of policy networks, and the multi-task capabilities of RL [5]. Group 4: Literature and Keywords - A list of relevant literature and keywords is provided for further exploration, indicating a rich field of study within RL and VLA [6].