bibtext: @inproceedings{
  shen2024nemoaligner,
  title={NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment},
  author={Gerald Shen and Zhilin Wang and Olivier Delalleau and Jiaqi Zeng and Yi Dong and Daniel Egert and Shengyang Sun and Jimmy J. Zhang and Sahil Jain and Ali Taghibakhshi and Markel Sanz Ausin and Ashwath Aithal and Oleksii Kuchaiev},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=yK2eGE8QVW}
  }
abstract: 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 challenging, especially for the largest and most competent LLMs which often contain tens or hundreds of billions of parameters. 
  We create NeMo-Aligner, a toolkit for model alignment that can efficiently scale to a thousand GPUs for training the largest open-source LLMs such as Nemotron 4 340B and Llama 3.1 405B. 
  NeMo-Aligner comes with highly optimized and scalable implementations for major paradigms of model alignment such as: Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), SteerLM,  and Self-Play Fine-Tuning (SPIN).
  Additionally, our toolkit supports running most of the alignment techniques in a Parameter Efficient Fine-Tuning (PEFT) setting.
  NeMo-Aligner is designed for extensibility, allowing support for other alignment techniques with minimal effort.
  It is open-sourced with Apache 2.0 License and we invite community contributions at https://github.com/NVIDIA/NeMo-Aligner.
title: NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment
authors: Gerald Shen Zhilin Wang Olivier Delalleau Jiaqi Zeng Yi Dong Daniel Egert Shengyang Sun Jimmy J. Zhang Sahil Jain Ali Taghibakhshi Markel Sanz Ausin Ashwath Aithal Oleksii Kuchaiev