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

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. We propose a human-efficient post-training pipeline that enables a small number of human operators to supervise multiple robots. The pipeline is built around a specialized division of labor: a trained Teleoperator focuses on high-value remote interventions and recovery demonstrations, while a Floor Operator monitors multiple robots, triggers takeovers, and performs physical resets. This role specialization reduces task switching, lowers operator training costs, and allows limited human labor to supervise more robot interaction across a larger fleet. To improve data utilization efficiency, we introduce VLAC-CUT as an automatic rollout curation tool. It segments autonomous robot trajectories into progress-making, idle, failure-inducing, and recovery portions, preserving useful segments while filtering harmful or uninformative ones. The curated rollout data are combined with Human-in-the-Loop data for the next post-training round. We validate the proposed pipeline on four real-world manipulation tasks. Across iterative post-training rounds, the final policies achieve 80\%--95\% success rates and improve task throughput by 1.7$\times$--4.2$\times$ over the base model. Under the same human-intervention budget, VLAC-CUT guided rollout reuse outperforms HITL-only training in both success rate and throughput.