arXiv:2510.09976v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models such as OpenVLA, Octo, and $\pi_0$ have shown strong generalization by leveraging large-scale demonstrations, yet their performance is still fundamentally constrained by the quality and coverage of supervised data. Reinforcement learning (RL) provides a promising path for improving and fine-tuning VLAs through online interaction. However, conventional policy gradient methods are computationally infeasible in the context of flow-matching based models due to the intractability of the importance sampling proce
The continuous evolution of large-scale AI models necessitates advanced fine-tuning, and the challenge with flow-matching policies highlights current method limitations.
Improving reinforcement learning techniques for Vision-Language-Action models will accelerate the development of more capable and adaptive AI systems, especially for embodied AI.
The ability to more effectively fine-tune VLA models through online interaction changes the trajectory of AI capabilities from purely data-driven to interaction-driven refinement.
- · AI research institutions
- · Robotics companies
- · Embodied AI developers
- · Developers reliant solely on supervised learning
- · Current reinforcement learning methodologies
More robust and generalizable Vision-Language-Action models will emerge.
This improved fine-tuning capability will accelerate the deployment of AI in complex, dynamic real-world environments.
The enhanced adaptability of AI could lead to more autonomous systems requiring less human intervention across various sectors.
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Read at arXiv cs.LG