Disentangled Representation Learning shows promise for enhancing AI's fundamental understanding of the world, potentially addressing hallucination issues in language models and improving controllability in generative systems. Despite significant academic interest, DRL research remains confined to synthetic scenarios due to a lack of realistic benchmarks and unified evaluation metrics. DRL4Real Workshop aims to bridge this gap by introducing novel, realistic datasets and comprehensive benchmarks for evaluating DRL methods in practical applications. We will focus on key areas including controllable generation and autonomous driving, exploring how DRL can advance model robustness, interpretability, and generalization capabilities.