aiqbench analysis
Miles v0.1 introduces a production-grade framework for frontier-scale reinforcement learning, emphasizing modularity, verification, and scalability across diverse model backends like NVIDIA Megatron-LM and PyTorch FSDP. By supporting techniques from full-parameter RL to LoRA and diffusion models, it aims to lower the barrier for enterprises deploying agentic systems on massive hardware clusters. This release represents a significant step toward operationalizing complex training loops in real-world environments.
Miles v0.1: Production-Level Post-Training
arXiv:2609.08368v1 Announce Type: new 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.
Title:Miles v0.1: Production-Level Post-Training
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 this https URL, with the project website at this https URL.
34 pages, 5 figures, 9 tables. Technical report
Machine Learning (cs.LG); Computation and Language (cs.CL)
arXiv:2609.08368 [cs.LG]
(or arXiv:2609.08368v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.08368 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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