SGLang and RadixArk Launch Open-Source RL Framework Miles v0.1
SGLang and RadixArk have released Miles v0.1, an open-source reinforcement-learning framework for post-training large language and multimodal models. It focuses on debugging reliability, hardware efficiency, and scalable execution.
SGLang and RadixArk have launched Miles v0.1, an open-source reinforcement-learning framework designed for post-training large language and multimodal models. The project aims to help teams validate training runs, use compute more efficiently, and operate RL workloads at scale.
The project says Miles combines SGLang for high-throughput rollout with Megatron-LM for scalable training. It includes asynchronous RL workflows, configurable on-policy and off-policy scheduling, and support for both NVIDIA and AMD hardware. Claims about contributor activity, model coverage, and production adoption come primarily from the project and its partners and have not been independently quantified in the cited material.
Source evidence
GitHub - radixark/miles: Miles is an enterprise-facing reinforcement learning framework for LLM and VLM post-training, forked from and co-evolving with slime. · GitHubgithub.com · supporting[2026/08] 🔥 Miles v0.1 is released! Read the blog post here: Miles v0.1: Production-level Post-training. [2026/07] Towards Blackwell-Native 8-bit and 4-bit RL: End-to-End MXFP8 and NVFP4 RL in Miles (blog). [2026/07] 🔥 SGLang and Miles add day-0 support for Kimi K3 (blog). [2026/07] On-policy distillation lands in Miles (blog). [2026/07] 🔥 SGLang and Miles add day-0 support for Inkling, a frontier multimodal model (blog). [2026/07] DeepSeek-V4 Flash RL training comes to AMD Instinct MI355X with Miles (blog). [2026/06] SGLang and Miles add day-0 support for NVIDIA Nemotron 3 Ultra (blog). [2026/05] No token left behind: token-in-token-out in Miles (blog). [2026/04] Updating 1 T parameters in seconds: P2P weight transfer in large-scale distributed RL (blog). [...] Fully async RL. Rollout and training workers are decoupled, with configurable on- and off-policy schedules, a pipeline tuned for fewer bubbles, and customizable async rollout and eval modes. See Fully Async RL. Fast
miles/README.md at main · radixark/miles · GitHubgithub.com · supporting[2026/08] 🔥 Miles v0.1 is released! Read the blog post here: Miles v0.1: Production-level Post-training. [2026/07] Towards Blackwell-Native 8-bit and 4-bit RL: End-to-End MXFP8 and NVFP4 RL in Miles (blog). [2026/07] 🔥 SGLang and Miles add day-0 support for Kimi K3 (blog). [2026/07] On-policy distillation lands in Miles (blog). [2026/07] 🔥 SGLang and Miles add day-0 support for Inkling, a frontier multimodal model (blog). [2026/07] DeepSeek-V4 Flash RL training comes to AMD Instinct MI355X with Miles (blog). [2026/06] SGLang and Miles add day-0 support for NVIDIA Nemotron 3 Ultra (blog). [2026/05] No token left behind: token-in-token-out in Miles (blog). [2026/04] Updating 1 T parameters in seconds: P2P weight transfer in large-scale distributed RL (blog). [...] ## About Miles is a high-performance, enterprise-ready reinforcement learning framework for large-scale model post-training. It pairs SGLang for high-throughput rollout with Megatron-LM for scalable training, and sh
🚀 Celebrating Partnership Success and Innovation! 🎉 It's ...instagram.com · supportingA huge congratulations to our long-term partner SGLang/RadixArk on the official launch of Miles v0.1! From the M-Series and H3 to Music 3
Yutong Wang's Post - LinkedInlinkedin.com · supportingToday we're launching Miles v0.1, an open-source RL framework for LLMs and multimodal models. RadixArk's mission is to make AI
Guohao Li 🐫 on X: "Huge congrats to the @radixark team on the launch of Miles v0.1! We've been using Miles to train RL agents for terminal coding and ML engineering, and to validate our RL environments across a wide range of training setups, both for open-source research like our SETA project (ht… / Xx.com · supporting## Post ## Post # Guohao Li 🐫 on X: "Huge congrats to the @radixark team on the launch of Miles v0.1! We've been using Miles to train RL agents for terminal coding and ML engineering, and to validate our RL environments across a wide range of training setups, both for open-source research like our SETA project ( and for our lab clients. So far we've trained across thousands of environments with models like DeepSeek V4 Flash, using Miles together with SGLang. Its efficient async RL support lets us iterate faster and focus on improving the quality of our agents and RL environment data. The Miles team has also been incredibly supportive throughout, an amazingly talented group to work with!" @guohao_li @radixark Avatar See this post in the app [...] @guohao_li @radixark Avatar See this post in the app Use the app to view all comments and discover more posts. @guohao_li @radixark @mingyilu123 @zz30gs @BanghuaZ
MiniMax (official) on X: "Congrats to our long-term partner SGLang/RadixArk on the launch of Miles v0.1! 🎉 From M-Series to H3 and Music 3, we’ve been closely building and pushing the OSS ecosystem forward together. Nothing beats the feeling of seeing things we built together keep growing @radix… / Xx.com · supporting## Post ## Post # MiniMax (official) on X: "Congrats to our long-term partner SGLang/RadixArk on the launch of Miles v0.1! 🎉 From M-Series to H3 and Music 3, we’ve been closely building and pushing the OSS ecosystem forward together. Nothing beats the feeling of seeing things we built together keep growing @radixark 🤝 @MiniMax\_AI" @MiniMax_AI @radixark ## Log in or sign up for X See what’s happening and join the conversation ## Relevant people Avatar ## Trending now @MiniMax_AI @radixark @zz30gs @kiri49x86 @eva_morgan_ai