The real world is multimodal. For AI to understand and recreate it, models need to learn across modalities. In our latest blog, we show how Miles supports that learning with a shared post-training design for VLMs and diffusion models. Link in the comments. 🔗

Shipping alongside Miles v0.1, Mooncake lands as a new rollout data-transfer backend in Miles, making remote GET 10-14× faster than the existing path. With @KVCache_AI, we gave the rollout-to-training handoff a dedicated data plane: - 1.2-1.6× faster PUT via structured-object transfer - Structure-aware PUT/GET optimizes serialization and bulk RDMA transfer, including zero-copy reconstruction from registered buffers on GET - Same put/get calls, no change to the RL programming model Read the full blog 👇 link in the comment

Today we're launching Miles v0.1, an open-source RL framework for LLMs and multimodal models. RL training is easy to start and hard to debug. Miles helps you ensure your run is correct, use hardware efficiently, and keep RL running at scale. Over the past 9 months, 72 contributors have landed 1,326 commits, 85 GPU E2E CI tests, battle-testing Miles on frontier open models like Kimi K3, DeepSeek V4, Qwen 3.8, GLM 5.2, Inkling, MiniMax H3, etc. Miles powers frontier-model development and production RL workloads at @humansand, @periodiclabs, @modal, @DecagonAI, @Eigent_AI, @nebiusai, @IBM and more, on both @NVIDIAAI and @AIatAMD hardware. Here is what we built, and why teams picked Miles🧵

RadixArk is joining the OpenEnv community. OpenEnv is the protocol layer for agent environments. It standardizes how environments are published, deployed, and consumed, so developers can mix any harness, any model, any inference engine on any task. This is exactly the kind of work we care about. Democratizing frontier AI means making the full stack, training included, open and usable by anyone. Excited to join the committee alongside @PyTorch @huggingface @nvidia @Microsoft @modal @UnslothAI @reflection_ai @PrimeIntellect @mercor_ai @fleet_ai and the rest of the open source community. We will start by integrating Miles with OpenEnv and shipping end-to-end examples that people can get their hands on. And there is more to come!

Great to be at the @hud_evals hackathon @ycombinator! We met old and new friends and were really impressed by everyone working on the hard problems in “RL” (reinforcement learning and real life)! We’re always hiring ambitious, amazing people who’d love to bring frontier RL infra to everyone. Come build with us!

Today, we are thrilled to officially launch RadixArk with $100M in Seed funding at a $400M valuation. The round was led by @Accel and co-led by @sparkcapital. RadixArk exists to make frontier AI infrastructure open and accessible to everyone. Today, the systems behind the most capable AI models are concentrated in a small number of companies. As a result, most AI teams are forced to rebuild training and inference stacks from scratch, duplicating the same infrastructure work instead of focusing on new models, products, and ideas. RadixArk was founded to change that. We are building an AI platform that makes it easier for teams to train and serve the best models at scale. RadixArk comes from the open-source community. We started with SGLang, where many of us are core developers and maintainers, and expanded our work to Miles for large-scale RL and post-training. We will continue contributing to both projects and working with the community to make them the strongest open-source infrastructure foundations for frontier AI. We would like to thank our long-term partners, contributors, and the broader SGLang community for believing in this mission. We're also grateful to @Accel and @sparkcapital, NVentures (Venture capital arm of @nvidia), Salience Capital, A&E Investment, @HOFCapital, @walden_catalyst, @AMD, LDVP, WTT Fubon Family, @MediaTek, Vocal Ventures, @Sky9Capital and our angel investors @ibab, @LipBuTan1, Hock Tan, @johnschulman2, @soumithchintala, @lilianweng, @oliveur, @Thom_Wolf, @LiamFedus, @robertnishihara, @ericzelikman, @OfficialLoganK, and @multiply_matrix among others. Thanks for the exclusive interview with @MeghanBobrowsky at @WSJ about our vision.