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RM-VLM uses vision-language models to ground LLM-generated reward machines

Researchers propose RM-VLM, a reinforcement-learning approach that has a large language model generate a reward machine from a natural-language task description. A vision-language model then serves as the labelling function, grounding the machine's propositions in image-based observations during policy learning. A VLM-based value estimator assesses progress toward reward-machine transitions and produces a dense reward signal. The method is evaluated on non-Markovian grid-world navigation, robotic manipulation, and 3D object collection, where it reports higher task success rates than existing VLM-based reward methods.