π_RL: Online RL Fine-tuning for Flow-based
  Vision-Language-Action Models
Authors: name: Kang Chen; name: Zhihao Liu; name: Tonghe Zhang; name: Zhen Guo; name: Si Xu; name: Hao Lin; name: Hongzhi Zang; name: Quanlu Zhang; name: Zhaofei Yu; name: Guoliang Fan; name: Tiejun Huang; name: Yu Wang; name: Chao Yu
Published: 2025-10-29 18:37:39

Abstract: Vision-Language-Action (VLA) models enable robots to understand and perform
complex tasks from multimodal input. Although recent work explores using
reinforcement learning (RL) to automate the laborious data collection process
in scaling supervised fine-tuning (SFT), applying large-scale RL to flow-based
VLAs (e.g., pi_0, pi_{0.5}) remains challenging due to intractable action
log-likelihoods from iterative denoising.
  We address this challenge with pi_{RL}, an open-source framework
for training flow-based VLAs in parallel simulation. pi_{RL}
implements two RL algorithms: (1) {Flow-Noise} models the denoising process as
a discrete-time MDP with a learnable noise network for exact log-likelihood
computation. (2) {Flow-SDE} integrates denoising with agent-environment
interaction, formulating a two-layer MDP that employs ODE-to-SDE conversion for
efficient RL exploration.
  We evaluate pi_{RL} on LIBERO and ManiSkill benchmarks. On LIBERO,
pi_{RL} boosts few-shot SFT models pi_0 and pi_{0.5} from 57.6%
to 97.6% and from 77.1% to 98.3%, respectively. In ManiSkill, we train
pi_{RL} in 320 parallel environments, improving pi_0 from 41.6% to
85.7% and pi_{0.5} from 40.0% to 84.8% across 4352 pick-and-place tasks,
demonstrating scalable multitask RL under heterogeneous simulation.
  Overall, pi_{RL} achieves significant performance gains and
stronger generalization over SFT-models, validating the effectiveness of online
RL for flow-based VLAs.