PRIME-RL/SimpleVLA-RL

Health v2 · maintenance only

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1834 stars · 119 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

SimpleVLA-RL is an open-source reinforcement learning framework for training Vision-Language-Action (VLA) models for robotic manipulation, built on veRL. It improves long-horizon action planning under data scarcity, achieving SoTA results on LIBERO and RoboTwin benchmarks and supporting real-world RL training.

Use cases

• train a VLA robot policy with reinforcement learning
• improve robotic manipulation beyond supervised fine-tuning
• run RL on LIBERO or RoboTwin simulation benchmarks
• reduce dependence on large-scale human demonstration data for robot learning
• apply RL to long-horizon dexterous manipulation tasks
• fine-tune OpenVLA-OFT with reinforcement learning

When to choose

• you need an efficient, VLA-specific RL training pipeline built on veRL
• you have limited demonstration data and want RL to boost generalization
• you want to reproduce or extend published SoTA VLA RL results
• you need multi-environment rendering and scalable parallelization for robot RL

When to avoid

• you only need simple supervised fine-tuning of a VLA model
• your robot stack is incompatible with supported VLA models like OpenVLA-OFT
• you lack GPU resources for large-scale RL training
• you need a general-purpose RL library unrelated to robotics

Facets

framework · maturity active

reinforcement-learning machine-learning llm-training simulation robotics reinforcement-learning machine-learning deep-learning python vla vision-language-action robot-manipulation verl robot-learning rlhf openvla gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="PRIME-RL/SimpleVLA-RL")