RARM: Confidence-Gated Progress Reward Modeling for RL in Manipulation.

arXiv:2606.22027v5 Announce Type: replace-cross Abstract: Reinforcement learning for robot manipulation is often bottlenecked by reward design, especially in long-horizon tasks: sparse success rewards provide weak supervision, while hand-crafted dense rewards