NVIDIA outlined Cosmos Framework for training and simulation workflows, plus NIM microservices for accelerated deployment of Cosmos models.
NVIDIA said it is expanding Cosmos with software aimed at two practical problems in physical AI: adapting world models to a developer’s sensors, tasks and data, and deploying those models reliably in applications.
In a developer blog published Nov. 2, 2026, NVIDIA described Cosmos Framework as an open, modular environment for world model training, model optimization and inference. The company said the framework provides agent skills and recipes for multimodal data processing, training, reinforcement learning, and deployment workflows. It also said Cosmos Framework is expanding beyond Cosmos 3 to support NVIDIA Alpamayo, GR00T, Qwen, pi0.5 and other compatible Hugging Face models.
NVIDIA said the framework supports training on text, images, video, audio and actions, with LoRA for parameter-efficient adaptation or full supervised fine-tuning to update all model parameters. It added that supported data inputs include JSONL, WebDataset, LeRobot and custom adapters, and that training can run across GPUs with checkpoints saved for resuming runs.
The company also said the platform now covers online and offline reinforcement learning paths, including DPO, GRPO, PPO, OPD and DiffusionNFT. According to the post, users can run rollout, reward and training roles on shared GPUs or separate pools, and connect supported simulators to collect experience.
NVIDIA highlighted several Cosmos simulation recipes built around Cosmos 3 Nano checkpoints. For autonomous vehicles, it said Cosmos3-Nano-Transfer-Auto uses bidirectional generation to denoise an entire multi-view clip at once for offline synthetic data generation.
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