Quick Summary: 📝
RLinf is an open-source infrastructure for post-training foundation models using reinforcement learning. It provides a flexible and scalable framework with features like macro-to-micro flow, flexible execution modes (collocated, disaggregated, hybrid), and auto-scheduling, supporting embodied agent development and integration with various VLA models and simulators.
Key Takeaways: 💡
✅ RLinf is a scalable infrastructure designed specifically for post-training large foundation models using Reinforcement Learning (Agentic AI).
✅ The M2Flow paradigm decouples logical training workflows (macro) from efficient physical resource scheduling and communication (micro).
✅ It features flexible execution modes (Collocated, Disaggregated, Hybrid) and uses an automatic scheduling strategy for optimal resource utilization.
✅ RLinf provides specialized, efficient support for training Embodied Agents and Vision-Language-Action (VLA) models.
✅ It is an open-source framework supporting both offline and advanced online reinforcement learning methodologies for continuous intelligence development.
Project Statistics: 📊
- ⭐ Stars: 565
- 🍴 Forks: 74
- ❗ Open Issues: 17
Tech Stack: 💻
- ✅ Python
If you've ever tried to train massive foundation models using Reinforcement Learning (RL)
how quickly scalability and efficiency become nightmares. Coordinating data collection, model updates, and distributed resources is incredibly tough, especially when dealing with complex tasks like robotics or embodied agents. RLinf is here to solve exactly that. It’s not just another RL library; it’s a robust, open-source infrastructure designed specifically for the post-training of these large models, making them evolve into smarter and more capable agents.
What makes RLinf so special is its innovative “Macro-to-Micro Flow” (M2Flow) paradigm. Think of it like separating the ‘what’ from the ‘how’. The ‘macro’ part is the logical workflow
the high-level steps you programmatically define for your training process. The ‘micro’ part handles the messy, low-level details: efficient physical communication, complex scheduling, and optimized resource management across your hardware. By decoupling these two layers, developers can focus purely on defining the learning logic without getting bogged down in distributed computing headaches. This paradigm makes complex, large-scale RL setups much easier to manage and customize, significantly boosting developer velocity.
This infrastructure offers amazing flexibility in how you utilize your hardware resources. It supports multiple execution modes, allowing you to tailor the system to your specific needs. You can use Collocated mode, where all GPUs are shared across workers; Disaggregated mode, which enables fine-grained pipelining for maximum throughput; or a Hybrid mode, combining the best aspects of both. The best feature, however, is the intelligent auto-scheduling strategy. Instead of manually tweaking resource allocations every time your workload changes, RLinf automatically selects the most suitable execution mode for optimal efficiency. This means faster experimentation and drastically less time spent on infrastructure boilerplate.
For developers diving into agentic AI, robotics, or Vision-Language-Action (VLA) models, RLinf is a game-changer. It provides specialized and fast adaptation support for mainstream VLA architectures like OpenVLA and
. Whether you are working with static offline datasets or moving into cutting-edge online reinforcement learning (which RLinf now officially supports!), this framework gives you the scalable backbone needed to push the boundaries of embodied intelligence. If you want your foundation model to evolve into a truly capable, continuously learning agent, RLinf provides the scalable, open-ended environment to make that happen efficiently.
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