Shypd.aiThe largest open index of AI toolsSimpleVLA-RL is an open-source reinforcement learning framework that scales Vision-Language-Action (VLA) model training. It offers an efficient end-to-end pipeline for improving long-horizon planning under data scarcity.
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SimpleVLA-RL is an open-source reinforcement learning (RL) framework designed to efficiently scale the training of Vision-Language-Action (VLA) models. It provides an end-to-end RL pipeline built on veRL, incorporating VLA-specific optimizations such as multi-environment parallel rendering for accelerated trajectory sampling. The framework leverages state-of-the-art infrastructure for efficient distributed training, hybrid communication patterns, and optimized memory management. SimpleVLA-RL supports various VLA models like OpenVLA and OpenVLA-OFT, and benchmarks including LIBERO and RoboTwin 1.0/2.0. It emphasizes minimal reward engineering with binary outcome rewards and includes exploration strategies like dynamic sampling and adaptive clipping. The modular architecture allows for easy integration of new VLA models, benchmarks, and RL algorithms, making it a powerful tool for researchers and developers in the field.
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