Reward Model Training Prompts for RLHF

NVIDIA's Nemotron-Cascade-RM-Training dataset provides 81,808 samples for training reward models in reinforcement learning from human feedback (RLHF). It contains prompts, data sources, and category information. The dataset was published by NVIDIA in December 2025.

Use Cases

• Training a reward model to score generated text based on the provided 'prompts' and associated preference data.
• Analyzing the distribution of 'data sources' and 'category' metadata to understand the composition and potential biases in the training corpus.
• Fine-tuning a base language model on the 'prompts' to generate candidate responses for subsequent preference ranking.

Strengths

• Contains 81,808 samples specifically curated for reward model training.
• Dataset is explicitly stated as ready for commercial use.

Limitations

• Specific column names, data formats, and sample structure are not provided in the input.
• The dataset is a curated subset of other datasets; the original sources and curation methodology are not detailed here.

Provenance

Source

NVIDIA

Collection Method

Curated subset of other datasets; specific methodology is not detailed.

Freshness

Last updated on the platform in December 2025.