AI RESEARCH
$\pi$-StepNFT: Wider Space Needs Finer Steps in Online RL for Flow-based VLAs
arXiv CS.CV
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ArXi:2603.02083v2 Announce Type: replace-cross 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{$\boldsymbol{\pi}$-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.