$\pi$-StepNFT: Wider Space Needs Finer Steps in Online RL for Flow-based VLAs
Overview
This research paper presents $\pi$-StepNFT, a flow-native online reinforcement learning framework designed for vision-language-action (VLA) models. It addresses the challenge of multi-step denoising by coupling wider stochastic exploration with finer step-wise supervision without computing final-action likelihood. The method treats observed stochastic differential equation (SDE) transitions as supervision units to improve policy updates. Experimental results on the LIBERO benchmark show success rates increasing from 57.6% to 92.0% for $\pi_0$ and from 77.1% to 94.0% for $\pi_{0.5}$ starting from shared few-shot SFT initializations. Additionally, on the ManiSkill benchmark, it achieves the highest reported mean success among evaluated RL baselines across five test-only visual perturbations.
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- arXiv Robotics — research abstractsSignal$\pi$-StepNFT: Wider Space Needs Finer Steps in Online RL for Flow-based VLAs
This research paper presents $\pi$-StepNFT, a flow-native online reinforcement learning framework designed for vision-language-action (VLA) models. It addresses the challenge of multi-step denoising by coupling wider stochastic exploration with finer step-wise supervision without computing final-action likelihood. The method treats observed stochastic differential equation (SDE) transitions as supervision units to improve policy updates. Experimental results on the LIBERO benchmark show success rates increasing from 57.6% to 92.0% for $\pi_0$ and from 77.1% to 94.0% for $\pi_{0.5}$ starting from shared few-shot SFT initializations. Additionally, on the ManiSkill benchmark, it achieves the highest reported mean success among evaluated RL baselines across five test-only visual perturbations.
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