Miles: A PyTorch-Native Stack for Large-Scale LLM RL Post-Training (pytorch.org)

RadixArk has announced Miles, an open-source framework designed specifically for large-scale reinforcement learning (RL) post-training of large language models (LLMs). This innovative stack integrates several advanced technologies, including SGLang for high-speed rollout generation, NVIDIA Megatron-LM for scalable training, and Ray for distributed orchestration, all while maintaining PyTorch as the foundation for extensibility and model management. Miles simplifies the complex coordination required in RL post-training by ensuring efficient communication between rollout workers and trainers, facilitating seamless low-precision operations, and providing robust error handling and observability. The significance of Miles lies in its ability to streamline the post-training process for modern, large-scale LLMs, particularly as they transition to more complex architectures like mixture-of-experts (MoE). As models grow larger and distributed training systems become more intricate, traditional RL frameworks can struggle to synchronize and manage resources effectively. Miles addresses this by offering a compact core that allows for substantial customization, ensuring that researchers can adapt the framework to emerging algorithms and production concerns without extensive forking. Its design enhances composability and reproducibility, ultimately driving forward the capabilities of RL in the AI/ML landscape.