Breaking long-horizon robotic tasks into atomic subtasks and using semantic-aware gating for credit assignment enables more efficient RL training of vision-language-action models, with practical real-world transfer via simulation.
AtomicRL improves vision-language-action (VLA) models for robot manipulation by using reinforcement learning with better credit assignment. It breaks long tasks into atomic subtasks, uses semantic-aware gating to prevent credit contamination between subtasks, and adaptively focuses learning on bottleneck skills.