This paper investigates Sim-to-Real generalization for dexterous manipulation tasks using Vision-Language-Action (VLA) models. The study empirically examines key factors affecting transfer from simulation to real-world deployment, including multi-level domain randomization, photorealistic rendering, physics-realistic modeling, and reinforcement learning updates. A comprehensive evaluation protocol is designed to quantify real-world manipulation performance, providing insights for developing generalist robot control policies that can effectively bridge the simulation-to-reality gap in dexterous manipulation scenarios.