### **Enterprise-Grade Reinforcement Learning for Large-Scale Model Post-Training**
[](https://miles.radixark.com/) [](https://github.com/radixark/miles)
[](https://miles.radixark.com/docs)
[](LICENSE)
[](https://slack.sglang.ai)
| [**Website**](https://miles.radixark.com/) | [**Documentation**](https://miles.radixark.com/docs) | [**Quick
Start**](https://miles.radixark.com/docs/getting-started/quick-start) | [**Supported Models**](https://miles.radixark.com/docs/models) | [**Miles
Diffusion**](https://github.com/radixark/miles_diffusion) | [**Blog**](https://www.lmsys.org/blog?filter=miles) | [**Slack**](https://slack.sglang.ai)
(`#miles-rl`) |
News
- [2026/08] 🔥 Miles v0.1 is released! Read the blog post here: [Miles v0.1: Production-level
Post-training](https://www.lmsys.org/blog/2026-08-18-miles-v0-1). - [2026/07] Towards Blackwell-Native 8-bit and 4-bit RL: End-to-End MXFP8 and NVFP4
RL in Miles (blog). - [2026/07] 🔥 SGLang and Miles add day-0 support for Kimi K3
(blog). - [2026/07] On-policy distillation lands in Miles
(blog). - [2026/07] 🔥 SGLang and Miles add day-0 support for Inkling, a frontier
multimodal model (blog). - [2026/07] DeepSeek-V4 Flash RL training comes to AMD Instinct
MI355X with Miles (blog). - [2026/06] SGLang and Miles add day-0 support for NVIDIA Nemotron
3 Ultra (blog). - [2026/05] No token left behind: token-in-token-out in Miles
(blog). - [2026/04] Updating 1 T parameters in seconds: P2P weight transfer in
large-scale distributed RL (blog). - [2026/04] 🔥 DeepSeek-V4 on day 0: from fast inference to
verified RL with SGLang and Miles (blog).
About
Miles is a high-performance, enterprise-ready reinforcement learning framework for large-scale model post-training. It pairs
SGLang for high-throughput rollout with Megatron-LM for scalable
training, and ships the precision, stability, and observability features an RL run needs at trillion-parameter scale. A PyTorch FSDP2 backend is
available for runs that would rather train the HuggingFace implementation as-is, though the recipes, the parallelism, and the largest models all live
on Megatron-LM. See Training Backends.
"A journey of a thousand miles begins with a single rollout."
Performance
- Fully async RL. Rollout and training workers are decoupled, with configurable on- and off-policy schedules, a pipeline tuned for fewer bubbles,
and customizable async rollout and eval modes. See Fully Async RL. - **Fast agentic
rollout.** Generation runs on SGLang behind a router that spreads requests across engines, preserves
per-request metadata, and health-checks the fleet. Tuned for multi-turn agentic workloads. - Fast weight updates. New weights reach the engines
in-loop in seconds, even on a trillion-parameter model such as Kimi-K2.6, with [P2P
RDMA](https://miles.radixark.com/docs/advanced/p2p-weight-transfer) as the fast path for disaggregated setups. - Low-precision training. [MXFP8
and NVFP4](https://miles.radixark.com/docs/advanced/low-precision) training with a numerically stable RL recipe that reduces precision-induced
divergence. FP8, INT4 QAT, BF16, and FP16 are also supported. - LoRA and multi-LoRA.
Low-rank adapters train frontier-scale models on a fraction of the GPUs, and the same adapters load
straight into SGLang for rollout.
Correctness and resilience
- Token-in-token-out (TITO). Supported for every model and every black-box harness,
with no detokenize/retokenize round-trip between rollout and training. - Rollout Routing Replay (R3). Expert routing recorded during rollout is
replayed in the trainer's forward pass, removing the MoE routing mismatch that destabilizes
large runs, with compute and communication overlapped to keep the cost down. - Fault tolerance. When an SGLang engine dies, Miles [recovers it and
resumes the run in place](https://miles.radixark.com/docs/advanced/fault-tolerance): no restart, no pause.
What Miles runs
- Day-0 model support. DeepSeek-V4, Kimi-K3, GLM-5.2, Inkling, and Nemotron landed on release day. Beyond day 0, nearly every frontier model runs
on Miles, including Kimi-K2.6 and Qwen3.5. See Models. - Extensive hardware support. NVIDIA GB300,
GB200, B300, B200, H200, H100, and A100, and AMD MI300X, MI325, MI350, and MI355X via ROCm. See
Installation for per-GPU status and the container image for
each. - Wide recipe support. GRPO, GSPO, PPO, and REINFORCE++ for RL, plus SFT and [on-policy
distillation](https://miles.radixark.com/docs/advanced/on-policy-distillation). - Agentic environments. Train coding and computer-use agents
through connectors for Harbor, HUD, NeMo Gym, OpenEnv, Verifiers, and more, each plugging into the rollout layer that fits it, with task sandboxes on
AgentENV, Daytona, E2B, or Modal. See Agentic Environments. - Diffusion models.
Flow-GRPO, DiffusionNFT and SFT on an sglang-diffusion rollout engine and an FSDP2 trainer, in
Getting Started
- Install Miles - [Quick
Start](https://miles.radixark.com/docs/getting-started/quick-start) - Supported Models - [Core
Concepts](https://miles.radixark.com/docs/user-guide/concepts) - Launch Script Walkthrough
- Training Backends - [Contribution
Guide](https://miles.radixark.com/docs/developer/contributor-guide)
Acknowledgment
Miles was forked from slime, and integrates SGLang,
Megatron-LM, and torch_memory_saver.
Miles is shaped by the teams that build on it and support its development, from hardware and cloud to model labs, agent infrastructure, and academia:
Citation
If Miles is useful in your research or your product, please cite it:
```bibtex @misc{miles2026, title = {Miles: Enterprise-Grade Reinforcement Learning for Large-Scale Model Post-Training}, author = {Miles Team}, year =
{2026}, howpublished = {\url{https://github.com/radixark/miles}} } ```