Editorial illustration for Nemotron 3 Super incorporates 40 million supervised and alignment samples
Nemotron 3 Super: AI Model Breakthrough in Reasoning
Nemotron 3 Super incorporates 40 million supervised and alignment samples
Forty million new supervised and alignment samples. That’s the raw fuel behind Nemotron 3 Super, not just more data, but a deliberate architecture of reasoning, instruction following, coding, safety, and multi-step agent tasks. NVIDIA didn’t stop at static text.
They built interactive RL across 21 environment configurations and 37 datasets, generating 1.2 million dynamic rollouts during training. Software engineer-style agent training. Tool-augmented search and planning.
Verifiable execution workflows. This is a model that learns by doing, not just by reading. And they’re publishing the tools and techniques openly, giving researchers and enterprises the freedom to customize or build their own reasoning models from the ground up.
The Mamba-Transformer MoE hybrid is the engine. But the real story is how they trained it: with scale, with environments, and with an eye toward agentic reasoning that actually works in the wild.
If you want to go hands on with Nemotron 3 Super, follow the tutorial video below.
This is not another model drop. It is a scaffold for reasoning that learns by doing, through code, through tool use, through multi-step failure and recovery. Forty million samples carved into supervised fine-tuning, preference data, and reinforcement trajectories.
Twenty-one environments generating over a million rollouts. The scale is not decorative; it is the point. NVIDIA has published the infrastructure, the environments, the training loops.
They are not handing you a finished answer. They are handing you the forge. Researchers and enterprises can customize Nemotron 3 Super or build their own reasoning models from the ground up.
The hybrid Mamba-Transformer MoE architecture is open. The agentic tasks are verifiable. The workflows are dynamic.
Static text-based training is a dead end for agentic reasoning. This is the alternative, interactive, iterative, grounded in execution. And it is available now.
The question is no longer whether you can build an agent that reasons. It is what you will build with the tools already in your hands.
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Common Questions Answered
How many supervised and alignment samples were used in Nemotron 3 Super's training?
Nemotron 3 Super incorporates 40 million supervised and alignment samples across various domains including reasoning, instruction following, coding, safety, and multi-step agent tasks. Approximately 7 million of these samples were directly used for supervised fine-tuning (SFT).
What makes Nemotron 3 Super's architecture unique in the AI model landscape?
Nemotron 3 Super features a hybrid Mamba-Transformer mixture-of-experts (MoE) architecture designed for agentic reasoning and efficient technical problem solving. This innovative design allows the model to handle complex multi-step tasks while maintaining operational efficiency across different computational environments.
What types of interactive environments were used in Nemotron 3 Super's reinforcement learning training?
The model was trained across 21 different environment configurations and 37 datasets, with approximately 10 of these datasets being publicly released. These environments included software engineer-style agent training and tool-augmented search and planning scenarios, demonstrating the model's versatility in complex reasoning tasks.
Further Reading
- Introducing Nemotron 3 Super: An Open Hybrid Mamba-Transformer MoE for Agentic Reasoning — NVIDIA Developer Blog
- Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer MoE Technical Report — NVIDIA Research
- Nemotron 3 Super: Pricing, Benchmarks, Architecture & API — LLM Stats
- NVIDIA Drops Nemotron 3 Super With 5x Throughput Gains for AI Agents — MEXC
