radixark/miles: README-based editorial guide

radixark/miles describes itself in the README as "Miles is an enterprise-facing reinforcement learning framework for LLM and VLM post-training, forked from and co-evolving with slime.". This article keeps to facts that can be checked in the repository. Stars, forks, and promotional badges are signals of attention, not proof of quality. Under "What is Miles?", the README says: Miles is a high-performance, enterprise-ready reinforcement learning (RL) framework specifically optimized for Large-Scale model Post-Training.. That establishes the project's stated boundary, not a production test.

The README's "Latest Updates" section gives a useful starting point for deciding whether the project fits: [2026/01] 💎 INT4 Quantization-Aware Training (QAT): Inspired by the Kimi K2-Thinking report, Miles now features a full-stack INT4 W4A16 QAT pipeline.. If that problem is not yours, popularity is a poor reason to adopt it. Project names, commands, and component names are kept as written so a reader can return to the primary source without guessing at terminology. Another checkable README item is: [2026/02] 💡 Miles Detailed Arguments: We've added a detailed command-line argument guide used to configure Miles for RL training and inference.. It can shape a first test, but it does not replace testing in the intended environment.

The operating model is spread across sections such as "🏗️ Supported Models". The source evidence includes: Miles supports a wide range of state-of-the-art architectures, with a special emphasis on DeepSeek, Qwen, Llama and mainstream models.. This article does not turn missing architecture, performance, or security details into claims. A real deployment still needs a look at the repository layout, configuration files, and release history.

Start installation from the README's documented entry point. A command that can be checked in the source is: # Pull the latest image docker pull radixark/miles:latest # Or install from source pip install -r requirements.txt pip install -e . When the README contains no runnable command, this article does not invent one. Open its "High-Performance Rollout • Low Precision Training • Production Stability" section and confirm system dependencies, default ports, and first-run initialization before using a public server.