A two-timescale RL post-training method that updates the semantic projection layer rarely and the action expert often improves VLA policy success on long-horizon manipulation tasks.
Open X-Embodiment: Robotic learning datasets and RT-X models,
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TEMPO: Semantic-Action Decoupled RL Post-Training for Vision-Language-Action Models
A two-timescale RL post-training method that updates the semantic projection layer rarely and the action expert often improves VLA policy success on long-horizon manipulation tasks.