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Miles is now featured on the PyTorch Foundation blog.
As models grow, shift from dense to MoE, and span more specialized hardware, RL post-training is no longer just about the algorithm. It is a distributed systems problem. Miles is our open-source RL training framework, built for exactly that.
It comprises four systems behind a small, pluggable trainer: SGLang (@sgl_project) for rollout, Megatron-LM (@NVIDIAAI) for training, Ray (@raydistributed) for orchestration, and PyTorch (@PyTorch) as the common layer for models and numerics.
Out of the box, you also get MoE-aware rollout/training alignment, a unified BF16/FP8/MXFP8/NVFP4/INT4-QAT pipeline, fast NCCL/RDMA weight sync, fault tolerance, and ready-to-run recipes for frontier models like DeepSeek V4, GLM 5.2, Qwen3.6, Kimi K2.6, and Nemotron 3 Ultra.
Our goal is simple: make frontier-scale LLM RL easier to reproduce, extend, and operate.
Thank you, PyTorch Foundation, and everyone who got Miles here, especially the legendary @slime_framework team!
PyTorch@PyTorch
Built on PyTorch, Ray, SGLang, and NVIDIA Megatron-LM, Miles is an open source framework from RadixArk for large-scale LLM reinforcement learning post-training. Miles uses PyTorch for models, numerics, profiling, and extensibility; Ray for orchestration; SGLang for rollout generation; and Megatron-LM for distributed training. The framework supports asynchronous rollout and training, NCCL/RDMA weight synchronization, MoE-aware rollout/training alignment, low-precision recipes, LoRA, fault tolerance, observability, and extension points for custom algorithms and model architectures. 🔗 Read more in our latest blog from the Miles Team: pytorch.org/blog/miles-a-p…
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