most citedLlama-Nemotron: Efficient Reasoning Models

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2025

NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model

NVIDIA, :, Aarti Basant +214

We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model designed to increase throughput for reasoning workloads while achieving state-of-the-art accuracy compar…

cs.CL2025★ 1 cited

Llama-Nemotron: Efficient Reasoning Models

Akhiad Bercovich, Itay Levy, Izik Golan +132

We introduce the Llama-Nemotron series of models, an open family of heterogeneous reasoning models that deliver exceptional reasoning capabilities, inference efficiency, and an ope…

cs.CL2025

Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models

NVIDIA, :, Aaron Blakeman +198

As inference-time scaling becomes critical for enhanced reasoning capabilities, it is increasingly becoming important to build models that are efficient to infer. We introduce Nemo…

cs.CV2025

Training Video Foundation Models with NVIDIA NeMo

Zeeshan Patel, Ethan He, Parth Mannan +26

Video Foundation Models (VFMs) have recently been used to simulate the real world to train physical AI systems and develop creative visual experiences. However, there are significa…

cs.CV2024

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…

cs.CV2024

StyleSplat: 3D Object Style Transfer with Gaussian Splatting

Sahil Jain, Avik Kuthiala, Prabhdeep Singh Sethi +1

Recent advancements in radiance fields have opened new avenues for creating high-quality 3D assets and scenes. Style transfer can enhance these 3D assets with diverse artistic styl…

cs.CL2024

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…