A new framework called Probe, Learn, Distill (PLD) is set to revolutionize the way vision-language-action models are improved. By utilizing residual reinforcement learning and innovative data collection methods, PLD addresses the limitations of traditional supervised fine-tuning, which often relies on expensive human demonstrations. This advancement not only enhances the scalability of these models but also boosts their generalization capabilities, making them more effective in real-world applications. The implications of this research could lead to significant improvements in AI systems that rely on understanding and interacting with visual and linguistic data.