Scaling Sim-to-Real VLA Reinforcement Learning with Generative 3D Worlds
The strong performance of large vision-language models (VLMs) trained with reinforcement learning (RL) has motivated similar approaches for fine-tuning vision-language-action (VLA) models in robotics. Many recent works fine-tune VLAs directly in the real world to avoid addressing the sim-to-real gap. While real-world RL circumvents sim-to-real issues, it inherently limits the generality of the resulting VLA, as scaling scene and object diversity in the physical world is prohibitively difficult. This leads to the paradoxical outcome of transforming a broadly pretrained model into an overfitted, scene-specific policy. Training in simulation can instead provide access to diverse scenes, but ...
| Authors | Andrew Choi, Xinjie Wang, Zhizhong Su et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 March 2026 |
| Last updated at source | 29 September 2026 |
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