Flow-based vision-language-action (VLA) models excel in embodied control but suffer from intractable likelihoods during multi-step sampling, hindering online reinforcement learning. We propose \textbf{\textit{π-StepNFT}} (Step-wise Negative-aware Fine-Tuning), a critic-and-likelihood-free framework that requires only a single forward pass per optimization step and eliminates auxiliary value networks. We identify that wider exploration spaces necessitate finer-grained, step-wise guidance for alignment. Empirically, π-StepNFT unlocks latent potential on LIBERO with competitive few-shot robustness. Moreover, it achieves superior generalization on ManiSkill, outperforming value-based baselines in OOD scenarios by preventing overfitting to multimodal features. This property offers a scalable solution promising for complex real-world applications.
@article{arxiv.2603.02083,
title = {$\pi$-StepNFT: Wider Space Needs Finer Steps in Online RL for Flow-based VLAs},
author = {Siting Wang and Xiaofeng Wang and Zheng Zhu and Minnan Pei and Xinyu Cui and Cheng Deng and Jian Zhao and Guan Huang and Haifeng Zhang and Jun Wang},
journal= {arXiv preprint arXiv:2603.02083},
year = {2026}
}