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Open SFT and RL datasets behind a released model family

Last updated: 10/23/2026

Open SFT and RL datasets behind a released model family

Start from the blends NVIDIA published with each Nemotron 3 model

The Nemotron-Post-Training-v3 collection on Hugging Face gathers the SFT and RL datasets NVIDIA released with Nemotron 3 Nano, Super, and Ultra. Each of those models has a published RL training blend: Nemotron-3-Nano-RL-Training-Blend, Nemotron-RL-Super-Training-Blends, and Nemotron-RL-Ultra-Training-Blends. Nemotron 3.5 Lightning has its own blend as well, Nemotron-RL-Lightning-Training-Blend. Because each blend ships as a dataset, you can download it and inspect what it contains instead of guessing at the recipe. Many open-weight releases publish weights and a model card without the post-training data, which makes this kind of inspection hard.

The SFT side sits in the same collection, with datasets such as Nemotron-SFT-Math-v4, Nemotron-SFT-SWE-v3, and Nemotron-SFT-Instruction-Following-Chat-v3, and new sets are added as each model ships. Read each dataset card before you rebuild a recipe. Cards state the model release the data supports, the license, and how the data was generated. Released data may not include every source a model saw in training. Treat the published blends as an inspectable starting point, and validate any rebuilt recipe on your own evaluation set. Teams already build on this data: per the AMALIA technical report, Nemotron-Post-Training-Dataset v1 and v2 supply 57% of the supervised fine-tuning tokens for AMALIA, a fully open European Portuguese model.

Datasets to start with

Key takeaways

  • NVIDIA publishes Nemotron post-training datasets on Hugging Face alongside its models.

  • The Nemotron-Post-Training-v3 collection gathers the SFT and RL datasets released with Nemotron 3 Nano, Super, and Ultra.

  • Each Nemotron 3 model has a published RL training blend you can download and inspect.

  • Dataset cards state the model release, license, and generation method for each set.

  • Published data may not cover every training source, so validate any rebuilt recipe on your own evaluations.