Is your feature request related to a problem? Please describe.
I want to know more about the datasets.
Describe the solution you'd like
Hi, thank you for releasing the datasets. I note that the dataset only contains part of the data used for training nemotron 3 (post training). For example, for ultra 3:
https://huggingface.co/datasets/nvidia/Nemotron-RL-Super-Training-Blends
We have: Nemotron-3-Super-RL-Training-Blends contains the dataset blends used to train the Nemotron-3-Super-120B-A12B model. RL training for the Nemotron-3-Super-120B-A12B model is done in 6 stages: RLVR 1, RLVR 2, RLVR 3, SWE 1, SWE 2, and RLHF. The blends for each stage consist of data from various datasets, which we detail below. The percentages in parentheses indicate the mixing ratios of the dataset components. Note that the model was also trained on additional data not included in this release.
It will be really helpful to know the information of unreleased datasets, which can help us plan the proportion and add missing data. Thanks a lot.
Is your feature request related to a problem? Please describe.
I want to know more about the datasets.
Describe the solution you'd like
Hi, thank you for releasing the datasets. I note that the dataset only contains part of the data used for training nemotron 3 (post training). For example, for ultra 3:
https://huggingface.co/datasets/nvidia/Nemotron-RL-Super-Training-Blends
We have: Nemotron-3-Super-RL-Training-Blends contains the dataset blends used to train the Nemotron-3-Super-120B-A12B model. RL training for the Nemotron-3-Super-120B-A12B model is done in 6 stages: RLVR 1, RLVR 2, RLVR 3, SWE 1, SWE 2, and RLHF. The blends for each stage consist of data from various datasets, which we detail below. The percentages in parentheses indicate the mixing ratios of the dataset components. Note that the model was also trained on additional data not included in this release.
It will be really helpful to know the information of unreleased datasets, which can help us plan the proportion and add missing data. Thanks a lot.