VLA-RFT: Vision-Language-Action Reinforcement Fine-tuning with Verified
  Rewards in World Simulators
Authors: name: Hengtao Li, name: Pengxiang Ding, name: Runze Suo, name: Yihao Wang, name: Zirui Ge, name: Dongyuan Zang, name: Kexian Yu, name: Mingyang Sun, name: Hongyin Zhang, name: Donglin Wang, name: Weihua Su

Vision-Language-Action (VLA) models enable embodied decision-making but rely
heavily on imitation learning, leading to compounding errors and poor
robustness under distribution shift. Reinforcement learning (RL) can mitigate
these issues yet typically demands costly real-world interactions or suffers
from sim-to-real gaps. We introduce VLA-RFT, a reinforcement fine-tuning
framework that leverages a data-driven world model as a controllable simulator.
Trained from real interaction data, the simulator predicts future visual
observations conditioned on actions, allowing policy rollouts with dense,
trajectory-level rewards derived from goal-achieving references. This design
delivers an efficient and action-aligned learning signal, drastically lowering
sample requirements. With fewer than 400 fine-tuning steps, VLA-RFT surpasses
strong supervised baselines and achieves greater efficiency than
simulator-based RL. Moreover, it exhibits strong robustness under perturbed
conditions, sustaining stable task execution. Our results establish
world-model-based RFT as a practical post-training paradigm to enhance the
generalization and robustness of VLA models. For more details, please refer to
https://vla-rft.github.io/.