> ## Documentation Index > Fetch the complete documentation index at: https://miles.radixark.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # Miles Documentation Miles is a high-performance, enterprise-ready reinforcement learning (RL) framework specifically optimized for **Large-Scale model Post-Training**. It couples [SGLang](https://github.com/sgl-project/sglang) for high-throughput rollout with [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) for scalable training, and ships the precision, stability, and observability features needed to run RL at trillion-parameter scale. *"A journey of a thousand miles begins with a single rollout."* — Miles focuses on the low-level system optimizations that make large-scale RL stable, efficient, and reproducible. ## Core features * **Fast and stable support for the latest models.** Day-0 enablement of frontier releases such as DeepSeek-V4, with rapid follow-on support for new architectures including GLM-5, Qwen 3.6, and Nemotron-3-Super. * **Unified low-precision training.** Customizable precision across the rollout and training engines, with unified **BF16**, **FP8**, **MXFP8**, and **INT4 QAT** recipes available now and an **NVFP4** training recipe in progress. * **Efficient Rollout Routing Replay (R3).** For MoE models, expert routing captured during inference is replayed during the trainer's forward pass, eliminating the mismatch that destabilizes large-scale MoE RL. Optimized with a routing-result cache and overlapped device-to-host (D2H) copy to reduce overhead in both single-turn and multi-turn RL. * **Speculative rollout with online MTP-SFT.** Miles keeps the draft model's acceptance rate high through training by fine-tuning MTP layers on-policy. * **LoRA training and serving.** Both SFT and RL recipes support LoRA adapters, and the same adapters load directly into SGLang for rollout — no separate merge or conversion step. * **Native agentic rollout.** Tool use, multi-turn dialogue, search, code execution, and multi-agent co-evolution are all supported through clean Python extension points. * **Minimal core, maximal extension.** Twenty-plus plug-points let you replace the rollout, reward, loss, or filter without forking the trainer. * **Broad hardware support.** First-class on NVIDIA Hopper (H100, H200) and Blackwell (B100, B200, GB200, GB300), with AMD MI300X / MI325 / MI350 / MI355X also supported via ROCm. ## Supported models Each model name links to its recipe page. | Family | Models | | --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **DeepSeek** | [DeepSeek-V4 Pro](/docs/models/deepseek/deepseek-v4-pro)
[DeepSeek-V4 Flash](/docs/models/deepseek/deepseek-v4-flash)
[DeepSeek-R1](/docs/models/deepseek/deepseek)
[DeepSeek-V3](/docs/models/deepseek/deepseek) | | **Thinking Machines** | [Inkling](/docs/models/thinkingmachines/inkling) | | **Qwen** | [Qwen3.6 MoE](/docs/models/qwen/qwen3-6-moe)
[Qwen3.6](/docs/models/qwen/qwen3-6)
[Qwen3.5-35B-A3B](/docs/models/qwen/qwen3-5-moe)
[Qwen3.5-4B / 9B / 27B](/docs/models/qwen/qwen3-5)
[Qwen3-Next-80B-A3B-Thinking](/docs/models/qwen/qwen3-next)
[Qwen3-30B-A3B / 235B-A22B](/docs/models/qwen/qwen3-moe)
[Qwen3-0.6B / 1.7B / 4B / 8B / 14B / 32B](/docs/models/qwen/qwen3) | | **GLM** | [GLM-5.1](/docs/models/glm/glm5)
[GLM-5](/docs/models/glm/glm5)
[GLM-4.7-Flash](/docs/models/glm/glm4-7-flash)
[GLM-4.5](/docs/models/glm/glm4-5)
[GLM-Z1-9B-0414](/docs/models/glm/glm4) | | **Kimi** | [Kimi-K2.6](/docs/models/kimi/kimi-k2.5)
[Kimi-K2.5](/docs/models/kimi/kimi-k2.5)
[Kimi-K2-Instruct / Thinking](/docs/models/kimi/kimi-k2)
[Moonlight-16B-A3B](/docs/models/kimi/moonlight) | | **Nemotron** | [Nemotron-3-Super-120B-A12B-FP8](/docs/models/nemotron/nemotron-3-super)
[Nemotron-3-Nano MoE](/docs/models/nemotron/nemotron-3-nano-moe)
[Nemotron-3-Nano](/docs/models/nemotron/nemotron-3-nano) | | **MiMo** | [MiMo-7B-RL](/docs/models/mimo/mimo) | | **GPT-OSS** | [gpt-oss-20b](/docs/models/gpt-oss/gpt-oss) | See [Models](/docs/models/index) for exact conversion commands, launch scripts, and parallelism settings. ## Supported hardware * **NVIDIA**: GB300, GB200, B200, B100, H200, H100, A100. * **AMD**: MI300X, MI325, MI350, MI355X (via ROCm). See [Platforms](/docs/platforms/index). ## Latest updates * **\[2026/02]** Complete argument reference. [CLI Reference](/docs/user-guide/cli-reference) * **\[2026/01]** INT4 W4A16 QAT. [INT4 Quantization-Aware Training](/docs/advanced/int4-qat) * **\[2026/01]** Unified VLM/LLM multi-turn rollout. [Multi-Agent Co-Evolution](/docs/examples/multi-agent) * **\[2025/12]** Rollout Routing Replay (R3) for MoE. [Rollout Routing Replay (R3)](/docs/advanced/miles-router) * **\[2025/11]** Unified FP8 pipeline generally available. [FP8 and Low Precision](/docs/advanced/fp8-low-precision) * **\[2025/11]** Speculative decoding with online MTP-SFT. [Speculative Decoding](/docs/advanced/speculative-decoding) ## Start here 1. **[Installation](/docs/getting-started/installation)** — Docker, bare metal, AMD. 2. **[Quick Start](/docs/getting-started/quick-start)** — a working training run in under an hour. 3. **[Core concepts](/docs/user-guide/concepts)** — the four objects in every Miles job. 4. **[Training backend](/docs/user-guide/usage)** — Megatron-LM, parallelism, checkpoints, and hooks. 5. **[Training script walkthrough](/docs/user-guide/training-script-walkthrough)** — every argument group in a launch script, annotated. ## Contribute * GitHub: [github.com/radixark/miles](https://github.com/radixark/miles) * Slack: [slack.sglang.ai](https://slack.sglang.ai), channel `#miles` * Contributing: [developer guide](/docs/developer/contributor-guide)