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NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment

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arxiv 2405.01481 v2 pith:VIOU6QQI submitted 2024-05-02 cs.CL cs.AIcs.LG

Gerald Shen , Zhilin Wang , Olivier Delalleau , Jiaqi Zeng , Yi Dong , Daniel Egert , Shengyang Sun , Jimmy Zhang
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Sahil Jain Ali Taghibakhshi Markel Sanz Ausin Ashwath Aithal Oleksii Kuchaiev
This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords alignmentnemo-alignerefficientllmsmodeltoolkitfine-tuninghuman
verification ladder T0 review T1 audit T2 compute T3 formal
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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

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Cited by 4 Pith papers

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    DistFlow decouples control and data flows in RL post-training, achieving near-linear scaling to 512 GPUs and up to 2.63x throughput vs. verl in tested workloads.

  3. The Challenge of Teaching Reasoning to LLMs Without RL or Distillation

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    Twenty high-quality chain-of-thought examples from a reasoning model are enough to activate strong math reasoning in a 32B base model with lightweight fine-tuning.

  4. AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training

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