Miles v0.1 Paper: Full-Stack Production Post-Training with LoRA and Diffusion Support

aigclink · x · 2026-09-13

The paper (arXiv:2609.08368) and GitHub repo for Miles v0.1 are now public. It is a full-stack, production-ready system for frontier post-training, building on slime's clean design and organizing every stage of the RL loop around one principle: components should be verified, clean, and customizable. Accuracy, efficiency, reliability, and scalability are first-class goals.

Architecture highlights

Rollout engines built on SGLang; trainer with two backends: NVIDIA Megatron-LM and PyTorch FSDP.
Three weight-synchronization transports for different deployment topologies.
Beyond full-parameter RL, it supports LoRA RL, on-policy distillation, supervised fine-tuning, and true-on-policy rollout-training alignment, extending the same architecture to diffusion models.
End-to-end case study: fully asynchronous agentic RL on GLM-5.2 744B-A40B for terminal-use coding tasks, on 64 NVIDIA GB300 GPUs, with a median step time of 263 seconds over the first 30 steps.

Code at github.com/radixark/miles; project site miles.radixark.com.

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Miles v0.1 Paper: Full-Stack Production Post-Training with LoRA and Diffusion Support

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