AcceRL: A Distributed Asynchronous Reinforcement Learning and World Model Framework for Vision-Language-Action Models
arXiv:2603.18464v3 Announce Type: replace Abstract: Reinforcement learning (RL) for large-scale Vision-Language-Action (VLA) models is severely bottlenecked by synchronization barriers and the high cost of environment data acquisition. To overcome these challenges, we propose AcceRL, a distributed asynchronous RL framework that physically isolates environment rollouts, model inference, and gradient updates. By eliminating the cascading long-tail idle bubbles inherent in synchronous systems, AcceRL maximizes hardware utilization and ensures scalable throughput. Furthermore, AcceRL features a mo
The increasing scale and complexity of Vision-Language-Action (VLA) models highlight the urgent need for more efficient reinforcement learning frameworks to overcome computational bottlenecks.
This development allows for more scalable and hardware-efficient training of large VLA models, accelerating progress in AI and potentially expanding their capabilities in real-world applications.
The shift from synchronous to distributed asynchronous reinforcement learning eliminates significant training bottlenecks, leading to faster iteration and deployment of advanced AI models.
- · AI model developers
- · Cloud computing providers
- · Robotics companies
- · Organizations deploying VLA models
- · Developers reliant on synchronous RL
- · Less efficient AI training platforms
Faster and cheaper training of large Vision-Language-Action models becomes possible.
This accelerates the development and deployment of more sophisticated AI agents capable of complex tasks.
The increased efficiency could democratize access to advanced AI development, fostering new applications across various industries.
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Read at arXiv cs.LG