The field of Vision-Language-Action (VLA) models is rapidly advancing, with a focus on improving robotic policy learning and multimodal understanding. Recent developments have centered around addressing the challenges of jointly predicting next-state observations and action sequences, as well as improving the efficiency and deployability of VLA models. Notably, researchers are exploring the use of energy-based models, diffusion transformers, and cross-modal knowledge sharing to enhance the performance of VLA models. These innovations have led to significant gains in tasks such as robotic policy learning and pursuit-evasion games. Furthermore, there is a growing interest in developing lightweight VLA models that can be efficiently deployed in real-time settings. Overall, the field is moving towards more robust, generalizable, and efficient VLA models that can be applied to a wide range of tasks. Some noteworthy papers in this area include: Dual-Stream Diffusion for World-Model Augmented Vision-Language-Action Model, which proposes a novel framework for handling modality conflicts in VLA models. EBT-Policy: Energy Unlocks Emergent Physical Reasoning Capabilities, which introduces an energy-based architecture that solves core issues in robotic and real-world settings. Equilibrium Policy Generalization: A Reinforcement Learning Framework for Cross-Graph Zero-Shot Generalization in Pursuit-Evasion Games, which proposes a framework for learning generalized policies with robust cross-graph zero-shot performance. From Static to Dynamic: Enhancing Offline-to-Online Reinforcement Learning via Energy-Guided Diffusion Stratification, which proposes a method for smoothing transitions in offline-to-online reinforcement learning. Evo-1: Lightweight Vision-Language-Action Model with Preserved Semantic Alignment, which presents a lightweight VLA model that reduces computation and improves deployment efficiency.