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Developers are combining frontier AI models such as GPT-6 Astra and Claude Fable 5 with the NVIDIA Omniverse libraries to build simulation applications through natural language instructions. The NVIDIA team demonstrated several projects: using Astra to build a warehouse humanoid robot simulator, Zero to Alpamayo, an autonomous driving simulation environment based on San Francisco's Market Street, and Robo Olympics, which has the Unitree G1 humanoid robot complete hurdles, where a single experiment successfully cleared hurdles 64 times out of 100 simulations. The Omniverse libraries provide GPU-accelerated physics, rendering, and sensor simulation capabilities.
Developers are combining frontier AI models such as GPT-6 Astra and Claude Fable 5 with the NVIDIA Omniverse libraries to build simulation applications through natural language instructions. The NVIDIA team demonstrated several projects: using Astra to build a warehouse humanoid robot simulator, Zero to Alpamayo, an autonomous driving simulation environment based on San Francisco's Market Street, and Robo Olympics, which has the Unitree G1 humanoid robot complete hurdles, where a single experime
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NVIDIA used the same fine-tuning recipe to turn Nemotron into a specialized model for competitive programming and math olympiad: at IOI 2026, Nemotron-3-Ultra-CC scored 535.4 points (out of 600), and the IMO system scored 30 points (out of 42), exceeding the official gold medal line. The recipe includes a strong base model, domain problems and reasoning trajectory data, SFT and RL post-training, and a generate-evaluate-improve inference loop. The related SFT and RL checkpoints, training datasets, the Nemotron-IMO-Bench benchmark, and the NeMo-Skills inference pipeline have been open-sourced on Hugging Face.
NVIDIA used the same fine-tuning recipe to turn Nemotron into a specialized model for competitive programming and math olympiad: at IOI 2026, Nemotron-3-Ultra-CC scored 535.4 points (out of 600), and the IMO system scored 30 points (out of 42), exceeding the official gold medal line. The recipe includes a strong base model, domain problems and reasoning trajectory data, SFT and RL post-training, and a generate-evaluate-improve inference loop. The related SFT and RL checkpoints, training datasets
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