# Miles v0.1: Production-Level Post-Training

Page: https://stenobird.com/podcast/daily-paper-cast-7079649/miles-v0-1-production-level-post-training
Text version: https://stenobird.com/podcast/daily-paper-cast-7079649/miles-v0-1-production-level-post-training.md
Podcast: [Daily Paper Cast](https://stenobird.com/podcast/daily-paper-cast-7079649)
Published: 2026-09-09T08:12:50+00:00
Episode link: https://share.transistor.fm/s/354e197c
Audio file: https://media.transistor.fm/354e197c/e68c2c6b.mp3
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JSON: https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/miles-v0-1-production-level-post-training
Duration seconds: 1234

## Resource

🤗 Upvotes: 29 | cs.LG, cs.CL Authors: RadixArk, Tom Chen, Mao Cheng, Shi Dong, Kangrui Du, Yanbin Jiang, Jiajun Li, Yiming Li, Tao Lin, Yusheng Su, Andy Ye, Yueming Yuan, Zhichen Zeng Title: Miles v0.1: Production-Level Post-Training Arxiv: http://arxiv.org/abs/2609.08368v1 Abstract: We present Miles v0.1, a full-stack, production-ready system for frontier post-training. Building upon the clean design of slime, Miles designs each stage of the reinforcement-learning (RL) training loop around a single principle: components should be verified, clean, and customizable. With accuracy, efficiency, reliability, and scalability as first-class goals, Miles aims to make frontier-scale RL accessible to researchers and enterprises alike. This report walks through the system end to end: rollout engines built on SGLang, a trainer with a choice of two backends (NVIDIA Megatron-LM and PyTorch FSDP), and three weight-synchronization transports for different deployment topologies. Beyond full-parameter RL, Miles also supports LoRA RL, on-policy distillation, supervised fine-tuning, and true-on-policy rollout-training alignment, and extends the same architecture to diffusion models. We close with an end-to-end case study: fully asynchronous agentic RL on a GLM-5.2 744B-A40B model over terminal-use coding tasks, running on 64 NVIDIA GB300 GPUs with a median step time of 263 seconds over the first 30 measured steps. Miles is open-sourced at https://github.com/radixark/miles, with the project website at https://miles.radixark.com.

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