EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models
The ability to efficiently and reliably learn new tasks has been a foundational challenge in robotics. Vision-Language-Action (VLA) models have demonstrated strong generalization across diverse manipulation tasks, yet pretrained policies consistently fall short of the reliability required for real-world deployment. Reinforcement learning (RL) fine-tuning offers a promising path to bridge this gap, but existing approaches either train from scratch without fully leveraging pretrained priors, or fine-tune VLAs without achieving the sample efficiency and success rates that practical deployment demands. We present EXPO-FT, a system for stable, sample-efficient RL finetuning of pretrained VLA p...
| Authors | Perry Dong, Kuo-Han Hung, Tian Gao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 25 May 2026 |
| Last updated at source | 17 August 2026 |
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