Researchers introduced SynthDemo-RL, a framework for fine-tuning Vision-Language-Action models using synthetic demonstrations generated by an automated teacher. This approach improves the performance of reinforcement learning with sparse rewards and enables successful task execution without human demonstrations. The framework demonstrates significant improvements on various benchmarks and validates its effectiveness on a physical robot.