Ordinal Stage-State Interface Outperforms Text in VLA Continuation Fine-Tuning

Jul 16, 2026

A preprint studying the LIBERO-10 robotics benchmark compares three ways of providing stage information to a VLA (Vision-Language-Action) policy: full-task instruction, current-stage text, and ordinal stage-state. Results show that while full-task instruction achieves the highest mean success rate (57.45%) under direct fine-tuning, the ordinal stage-state interface outperforms both alternatives under continuation fine-tuning, achieving 53.75% mean success rate and exceeding the others in all paired runs.

Why it matters: This work demonstrates that the effectiveness of explicit stage information in VLA policy fine-tuning depends on both the interface representation and the training setup.

Full story at: arXiv Robotics ↗