7 citations · 15 across the 14 of their papers we have counts for
5 papers · 1 filter
HelpSteer2-Preference: Complementing Ratings with Preferences
Zhilin Wang, Alexander Bukharin, Olivier Delalleau +5
Reward models are critical for aligning models to follow instructions, and are typically trained following one of two popular paradigms: Bradley-Terry style or Regression style. Ho…
Elucidating Optimal Reward-Diversity Tradeoffs in Text-to-Image Diffusion Models
Rohit Jena, Ali Taghibakhshi, Sahil Jain +3
Text-to-image (T2I) diffusion models have become prominent tools for generating high-fidelity images from text prompts. However, when trained on unfiltered internet data, these mod…
HelpSteer2: Open-source dataset for training top-performing reward models
Zhilin Wang, Yi Dong, Olivier Delalleau +6
High-quality preference datasets are essential for training reward models that can effectively guide large language models (LLMs) in generating high-quality responses aligned with…
Nemotron-4 340B Technical Report
Nvidia, :, Bo Adler +80
We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward. Our models are open access under the NVIDIA Open…
NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment
Gerald Shen, Zhilin Wang, Olivier Delalleau +10
Aligning Large Language Models (LLMs) with human values and preferences is essential for making them helpful and safe. However, building efficient tools to perform alignment can be…