mlsanityData to 7 Oct 2026

NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment

arXiv 2405.01481 ↗First posted 2 May 2024cs.CL cs.AI cs.LGcited by 40

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

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

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 18 of its 38 references that are in the map. First posted on arXiv 2 May 2024.

Builds on

18 of 38 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023A General Theoretical Paradigm to Understand Learning from Human Preferences18 Oct
  2. 2022Training language models to follow instructions with human feedback4 Mar

Other references · 16

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  5. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  6. 2017Deep reinforcement learning from human preferencescited by 3,846
  7. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  8. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  9. 2020Learning to summarize from human feedbackcited by 2,562
  10. 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,829
  11. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  12. 2023Enhancing Chat Language Models by Scaling High-quality Instructional Conversationscited by 825
4 more
  1. 2019NeMo: a toolkit for building AI applications using Neural Modulescited by 230
  2. 2023DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scalescited by 107
  3. 2023SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHFcited by 107
  4. 2023The Alignment Ceiling: Objective Mismatch in Reinforcement Learning from Human Feedbackcited by 42

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 21; 2026: 13.

40 papers.

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By area:

  1. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 1,980
  2. 2024Tulu 3: Pushing Frontiers in Open Language Model Post-Trainingcited by 988
  3. 2025AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoningcited by 234
  4. 2025AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning datasetcited by 165
  5. 2025Llama-Nemotron: Efficient Reasoning Modelscited by 151
  6. 2025OpenCodeReasoning: Advancing Data Distillation for Competitive Codingcited by 106
  7. 2024OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Frameworkcited by 70
  8. 2024Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Modelscited by 65
  9. 2025AgentRL: Scaling Agentic Reinforcement Learning with a Multi-Turn, Multi-Task Frameworkcited by 58
  10. 2025RLinf: Flexible and Efficient Large-scale Reinforcement Learning via Macro-to-Micro Flow Transformationcited by 51
  11. 2025LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Trainingcited by 37
  12. 2025AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Trainingcited by 37
28 more
  1. 2025When2Call: When (not) to Call Toolscited by 36
  2. 2025Laminar: A Scalable Asynchronous RL Post-Training Frameworkcited by 35
  3. 2024ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocationcited by 34
  4. 2024Adapting Multilingual LLMs to Low-Resource Languages using Continued Pre-training and Synthetic Corpuscited by 24
  5. 2025Seer: Online Context Learning for Fast Synchronous LLM Reinforcement Learningcited by 21
  6. 2025VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoocited by 20
  7. 2025Adversarial Training of Reward Modelscited by 17
  8. 2025HelpSteer3: Human-Annotated Feedback and Edit Data to Empower Inference-Time Scaling in Open-Ended General-Domain Taskscited by 16
  9. 2025Generative AI Act II: Test Time Scaling Drives Cognition Engineeringcited by 14
  10. 2025RLBoost: Harvesting Preemptible Resources for Cost-Efficient Reinforcement Learning on LLMscited by 10
  11. 2025MindSpeed RL: Distributed Dataflow for Scalable and Efficient RL Training on Ascend NPU Clustercited by 9
  12. 2026Accelerating RL Post-Training Rollouts via System-Integrated Speculative Decodingcited by 8
  13. 2026Understanding and Exploiting Weight Update Sparsity for Communication-Efficient Distributed RLcited by 5
  14. 2025Aligning Large Language Models to Low-Resource Languages through LLM-Based Selective Translation: A Systematic Studycited by 4
  15. 2026SortedRL: Accelerating RL Training for LLMs through Online Length-Aware Schedulingcited by 4
  16. 2025OPPO: Accelerating PPO-based RLHF via Pipeline Overlapcited by 3
  17. 2026AIS: Adaptive Importance Sampling for Quantized RLcited by 2
  18. 2025The Challenge of Teaching Reasoning to LLMs Without RL or Distillationcited by 1
  19. 2026MTDrive: Multi-turn Interactive Reinforcement Learning for Autonomous Drivingcited by 1
  20. 2026RLHFless: Serverless Computing for Efficient RLHFcited by 1
  21. 2026MinT: Managed Infrastructure for Training and Serving Millions of LLMscited by 1
  22. 2026AstraFlow: Dataflow-Oriented Reinforcement Learning for Agentic LLMscited by 1
  23. 2026Rollplex: Cross-Phase GPU Spatial Sharing for Vision Language Model Post-Trainingcited by 1
  24. 2025TCIA: A Task-Centric Instruction Augmentation Method for Instruction Finetuningcited by 0
  25. 2026ARL-Tangram: Unleash the Resource Efficiency in Agentic Reinforcement Learningcited by 0
  26. 2026PlexRL: Cluster-Level Orchestration of Serviceized LLM Execution for RLVRcited by 0
  27. 2026GrowMTP: Can RL Grow Its Own Draft Head?cited by 0
  28. 2026WeightBridge: An Efficient Weight Transfer Library for Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Aligner: Efficient Alignment by Learning to Correct
  2. 2025Align-Pro: A Principled Approach to Prompt Optimization for LLM Alignment
  3. 2024Efficient Alignment of Large Language Models via Data Sampling
  4. 2025SDA: Steering-Driven Distribution Alignment for Open LLMs without Fine-Tuning
  5. 2025Aligning Frozen LLMs by Reinforcement Learning: An Iterative Reweight-then-Optimize Approach
  6. 2023Aligner: One Global Token is Worth Millions of Parameters When Aligning Large Language Models
  7. 2025DiffPO: Diffusion-styled Preference Optimization for Efficient Inference-Time Alignment of Large Language Models
  8. 2024Aligners: Decoupling LLMs and Alignment