VINE: Taming Generative Control Policies for Reinforcement Learning

Jul 14, 2026

Researchers introduce VINE, a reinforcement learning-oriented sampling method that enables stable end-to-end value-gradient optimization for flow-matching policies. VINE reconstructs a new interpolation state at each denoising step, creating a stable differentiable path for value-gradient propagation. The method preserves the expressiveness and iterative generation of flow-matching policies and achieves stable policy improvement. VINE outperforms state-of-the-art RL methods on the OGBench offline RL benchmark and real-world robotic manipulation tasks.

Why it matters: This work addresses a key source of instability in training expressive generative control policies for reinforcement learning, enabling more robust and scalable robot learning.

Full story at: arXiv Robotics ↗