This paper presents RoboAlign, a systematic framework for improving vision-language-action models (VLAs) by enhancing embodied reasoning capabilities in multimodal large language models (MLLMs). The key innovation involves sampling action tokens through zero-shot natural language reasoning and refining them using reinforcement learning to improve action accuracy. The approach effectively bridges the modality gap between language understanding and low-level robot actions, facilitating knowledge transfer from multimodal LLMs to embodied agents. Experimental validation demonstrates that training VLAs with this framework leads to reliable performance improvements in robotic manipulation tasks.