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阿里云SGLang 与 RadixArk 正式推出 Miles v0.1,用于大语言模型和多模态模型的强化学习后训练。该框架主打训练调试、硬件利用率和大规模运行效率。
SGLang/RadixArk 发布了 Miles v0.1,这是一个面向大语言模型和多模态模型后训练的开源强化学习框架。项目旨在帮助开发团队验证训练流程、提高硬件利用率,并在更大规模上运行强化学习任务。
根据项目方介绍,Miles 将 SGLang 的高吞吐推理与 Megatron-LM 的可扩展训练能力结合,并支持异步强化学习、可配置的在线与离线策略,以及 NVIDIA 和 AMD 硬件。项目方还表示,框架已在多种开源模型和企业级工作负载中进行测试;这些采用情况和性能描述目前主要来自项目团队及合作方。
来源核查不代表信息永远正确;请结合原文、观察时间与后续修正判断。
[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
[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
A 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
Today we're launching Miles v0.1, an open-source RL framework for LLMs and multimodal models. RadixArk's mission is to make AI
## 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
## 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