Flow-based vision-language-action (VLA) models have emerged as a powerful paradigm for embodied control, but multi-step denoising makes policy-gradient online reinforcement learning (RL) difficult because the final-action likelihood induced by the sampler is not directly tractable. We identify an exploration--supervision mismatch within the denoising trajectory, where stochastic differential equation (SDE) sampling explores a wider local stochastic transition space while supervision derived from the final denoised output does not directly use the adjacent transitions observed during sampling. We propose $π$-StepNFT (Step-wise Negative-aware Fine-Tuning), a flow-native online RL framework that couples wider stochastic exploration with finer step-wise supervision without computing the final-action likelihood. $π$-StepNFT treats observed SDE transitions as supervision units, using variance-normalized transition errors to rank mirrored positive and negative velocity branches and map rollout feedback to local velocity updates. On LIBERO, starting from shared few-shot SFT initializations, $π$-StepNFT raises average success from 57.6\% to 92.0\% for $π_0$ and from 77.1\% to 94.0\% for $π_{0.5}$. On ManiSkill, it achieves the highest reported mean success among the evaluated RL baselines on five test-only visual perturbations. Our project homepage is available at https://wangst0181.github.io/pi-StepNFT/.

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