CoreWeave推出RL Rollouts服务
CoreWeave和NVIDIA联手让模型重载快了15倍，训练效率大幅提升。
CoreWeave与NVIDIA合作推出RL Rollouts服务，使用ModelExpress和Router技术加速模型重载。在Nemotron 3.5 Lightning后训练过程中，实现了比基线快15倍的模型重载速度。该服务解决了强化学习后训练中反复加载模型权重导致的GPU等待问题。
Congrats @CoreWeave on RL Rollouts! RL post-training involves a lot of back and forth: train the model, generate responses, then train again. Inference workers need to load the updated model weights each time. As models get bigger, that can leave GPUs waiting. CoreWeave’s new service uses ModelExpress and Router in NVIDIA Dynamo to speed up those reloads with minimal downtime. Working with us and @youdotcom , CoreWeave achieved 15× faster model reloads compared with its baseline while post-training Nemotron 3.5 Lightning. Check out their blog below for details CoreWeave @CoreWeave ICYMI: CoreWeave Forge is here 🎉 A production trace that never reaches the next training run is a signal you paid for and threw away. Most teams do it every day, because the tool that catches the trace and the tool that runs the training came from different vendors and were never built to talk. Forge closes that gap by unifying @wandb , post-training from @OpenPipeAI , and @marimo_io notebooks in one connected environment with CoreWeave Training, Inference, Sandboxes, and Registry. Run, observe, curate, improve, evaluate. The traces you flag in production become the datasets you train on and the evaluations you gate with. Open across any model, framework, or cloud. @MasterClass and @canva are already building on it. Free, Pro, and Enterprise available today: crwv.co/utcAw p Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 0 🔄 0 ❤️ 3 👀 886 ⚡
