WARP-RM: A Warp-Augmented Relative Progress Reward Model for Data Curation

arXiv:2606.28320v1 Announce Type: new Abstract: Scaling imitation learning requires large datasets, yet human teleoperation inevitably produces mixed-quality demonstrations containing hesitations and recoveries. Prior frame-level progress reward models supervise on absolute temporal progress proxies that suffer from label noise, or require costly human annotations to define subtask boundaries. We present WARP (Warp-Augmented Relative Progress), a novel fully self-supervised algorithm for learnin...