Miles natively supports @sgl_project for fast rollouts, while keeping rollout and training aligned for reliable post-training at scale.
Grateful to the community for the contributions and feedback that continue to shape Miles.
RadixArk says its Miles framework natively supports SGLang for fast rollouts while keeping rollout and training aligned for reliable post-training at scale. The post thanks the community for contributions and feedback shaping Miles. A related post from @adarshxs describes Miles v0.1 running fully async agentic RL on a 744B MoE across 64 GB300 GPUs.
Miles natively supports @sgl_project for fast rollouts, while keeping rollout and training aligned for reliable post-training at scale.
Grateful to the community for the contributions and feedback that continue to shape Miles.
Huge congrats to the @radixark team on Miles v0.1! 🚀 Fully async agentic RL on a 744B MoE across 64 GB300s, with @sgl_project powering rollouts. Love the focus on getting rollout/training fidelity right alongside throughput. https://huggingface.co/papers/2609.08368View quoted post on X
Source: RadixArk · x.comPublished · added here