Maximizing Human Efficiency in Large-Scale Robot Post-Training via VLAC-Cut Guided Pipeline

arXiv:2607.09776v1 Announce Type: new Abstract: When adapting Vision Language Action (VLA) models to downstream tasks, multiple rounds of post training are required because a single round of data cannot resolve all issues, making continuous iterations necessary to progressively address the weaknesses exposed in previous rounds. In this report, we aim to maximize human efficiency during post-training, defined as the policy improvement and task throughput achieved per unit of human labor and time....