VINE: Taming Generative Control Policies for Reinforcement Learning
Flow-matching policies have emerged as an effective policy parameterization for robot learning. They iteratively generate actions from noise, enabling highly expressive modeling of complex and multimodal action distributions. However, prior works observed that scaling these policies with value-gradient reinforcement learning (RL) often leads to training instability. Existing methods attribute this instability to iterative generation and therefore avoid end-to-end value-gradient optimization by sacrificing iterative generation, high expressiveness, or value-gradient optimization. Contrary to prior belief, we show the instability does not stem from iterative generation itself, but from the ...
| Authors | Rushuai Yang, Zhuo Han, Houlin Li et al. |
|---|---|
| Primary category | cs.RO |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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