mlsanityData to 7 Oct 2026

Aligner: Efficient Alignment by Learning to Correct

arXiv 2402.02416 ↗First posted 4 Feb 2024cs.CL cs.AI cs.LGcited by 96

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

Abstract (arXiv)

With the rapid development of large language models (LLMs) and ever-evolving practical requirements, finding an efficient and effective alignment method has never been more critical. However, the tension between the complexity of current alignment methods and the need for rapid iteration in deployment scenarios necessitates the development of a model-agnostic alignment approach that can operate under these constraints. In this paper, we introduce Aligner, a novel and simple alignment paradigm that learns the correctional residuals between preferred and dispreferred answers using a small model. Designed as a model-agnostic, plug-and-play module, Aligner can be directly applied to various open-source and API-based models with only one-off training, making it suitable for rapid iteration. Notably, Aligner can be applied to any powerful, large-scale upstream models. Moreover, it can even iteratively bootstrap the upstream models using corrected responses as synthetic human preference data, breaking through the model's performance ceiling. Our experiments demonstrate performance improvements by deploying the same Aligner model across 11 different LLMs, evaluated on the 3H dimensions (helpfulness, harmlessness, and honesty). Specifically, Aligner-7B has achieved an average improvement of 68.9% in helpfulness and 23.8% in harmlessness across the tested LLMs while also effectively reducing hallucination. In the Alpaca-Eval leaderboard, stacking Aligner-2B on GPT-4 Turbo improved its LC Win Rate from 55.0% to 58.3%, surpassing GPT-4 Omni's 57.5% Win Rate (community report).

Key facts. Cited by 96 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 61 of its 80 references that are in the map. First posted on arXiv 4 Feb 2024.

Builds on

61 of 80 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Black-Box Prompt Optimization: Aligning Large Language Models without Model Training7 Nov
  2. 2022Training language models to follow instructions with human feedback4 Mar
  3. 2021A General Language Assistant as a Laboratory for Alignment1 Dec

Other references · 58

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2017Proximal Policy Optimization Algorithmscited by 14,441
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  5. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  6. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  7. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  8. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  9. 2020Scaling Laws for Neural Language Modelscited by 6,411
  10. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  11. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  12. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
38 more
  1. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  2. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  3. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  4. 2023A Survey of Large Language Modelscited by 2,537
  5. 2022Emergent Abilities of Large Language Modelscited by 2,483
  6. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  7. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  8. 2016Concrete Problems in AI Safetycited by 1,582
  9. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
  10. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,259
  11. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,239
  12. 2023PaLM 2 Technical Reportcited by 1,154
  13. 2023Teaching Large Language Models to Self-Debugcited by 963
  14. 2023BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Datasetcited by 828
  15. 2023Steering Language Models With Activation Engineeringcited by 824
  16. 2019Plug and Play Language Models: A Simple Approach to Controlled Text Generationcited by 793
  17. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 743
  18. 2023Aligning Large Multimodal Models with Factually Augmented RLHFcited by 703
  19. 2022Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learnedcited by 702
  20. 2023Baichuan 2: Open Large-scale Language Modelscited by 664
  21. 2023Safe RLHF: Safe Reinforcement Learning from Human Feedbackcited by 630
  22. 2023RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedbackcited by 615
  23. 2023LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusioncited by 550
  24. 2023Reinforced Self-Training (ReST) for Language Modelingcited by 486
  25. 2023RRHF: Rank Responses to Align Language Models with Human Feedback without tearscited by 454
  26. 2023Large Language Models Are Not Robust Multiple Choice Selectorscited by 454
  27. 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 445
  28. 2023Scalable Extraction of Training Data from (Production) Language Modelscited by 415
  29. 2023RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedbackcited by 404
  30. 2021FUDGE: Controlled Text Generation With Future Discriminatorscited by 374
  31. 2022Self-critiquing models for assisting human evaluatorscited by 372
  32. 2023Toxicity in ChatGPT: Analyzing Persona-assigned Language Modelscited by 356
  33. 2023Multi-step Jailbreaking Privacy Attacks on ChatGPTcited by 320
  34. 2023AI Alignment: A Comprehensive Surveycited by 256
  35. 2022Extracting Latent Steering Vectors from Pretrained Language Modelscited by 217
  36. 2023Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignmentcited by 213
  37. 2021DialogSum: A Real-Life Scenario Dialogue Summarization Datasetcited by 181
  38. 2018Supervising strong learners by amplifying weak expertscited by 139

50 most cited of 58 All 58 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 16; 2025: 60; 2026: 20.

96 papers.

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  1. 2024A Survey on Knowledge Distillation of Large Language Modelscited by 276
  2. 2025GuardReasoner: Towards Reasoning-based LLM Safeguardscited by 89
  3. 2025STAIR: Improving Safety Alignment with Introspective Reasoningcited by 72
  4. 2025SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learningcited by 69
  5. 2024What is the Role of Small Models in the LLM Era: A Surveycited by 41
  6. 2024Assessment of Multimodal Large Language Models in Alignment with Human Valuescited by 36
  7. 2024SafeSora: Towards Safety Alignment of Text2Video Generation via a Human Preference Datasetcited by 34
  8. 2024JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capabilitycited by 32
  9. 2024How Far Are We From AGI: Are LLMs All We Need?cited by 31
  10. 2025Safe RLHF-V: Safe Reinforcement Learning from Multi-modal Human Feedbackcited by 27
  11. 2025A Survey on Personalized and Pluralistic Preference Alignment in Large Language Modelscited by 25
  12. 2025Equilibrate RLHF: Towards Balancing Helpfulness-Safety Trade-off in Large Language Modelscited by 22
38 more
  1. 2025SoK: Evaluating Jailbreak Guardrails for Large Language Modelscited by 17
  2. 2025HelpSteer3: Human-Annotated Feedback and Edit Data to Empower Inference-Time Scaling in Open-Ended General-Domain Taskscited by 16
  3. 2025Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularizationcited by 14
  4. 2025Think Twice Before You Act: Enhancing Agent Behavioral Safety with Thought Correctioncited by 14
  5. 2025On Almost Surely Safe Alignment of Large Language Models at Inference-Timecited by 13
  6. 2025Weak-to-Strong Generalization Even in Random Feature Networks, Provablycited by 13
  7. 2025Disambiguate First, Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic Parsingcited by 12
  8. 2025CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steeringcited by 12
  9. 2024The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignmentcited by 11
  10. 2025Persona-judge: Personalized Alignment of Large Language Models via Token-level Self-judgmentcited by 11
  11. 2025Self-Destructive Language Modelcited by 10
  12. 2025Multi-objective Large Language Model Alignment with Hierarchical Expertscited by 10
  13. 2024Reward Generalization in RLHF: A Topological Perspectivecited by 9
  14. 2024Sequence to Sequence Reward Modeling: Improving RLHF by Language Feedbackcited by 9
  15. 2025SafeLawBench: Towards Safe Alignment of Large Language Modelscited by 9
  16. 2025Too Helpful, Too Harmless, Too Honest or Just Right?cited by 9
  17. 2024ConTrans: Weak-to-Strong Alignment Engineering via Concept Transplantationcited by 8
  18. 2024Efficient Model-agnostic Alignment via Bayesian Persuasioncited by 8
  19. 2025LED-Merging: Mitigating Safety-Utility Conflicts in Model Merging with Location-Election-Disjointcited by 7
  20. 2025Inference-time Alignment in Continuous Spacecited by 7
  21. 2025Well Begun is Half Done: Low-resource Preference Alignment by Weak-to-Strong Decodingcited by 7
  22. 2024Direct Alignment of Language Models via Quality-Aware Self-Refinementcited by 6
  23. 2024Learning to Correct for QA Reasoning with Black-box LLMscited by 6
  24. 2025Adaptive Helpfulness-Harmlessness Alignment with Preference Vectorscited by 6
  25. 2025Disentangled Safety Adapters Enable Efficient Guardrails and Flexible Inference-Time Alignmentcited by 6
  26. 2026Be Your Own Red Teamer: Safety Alignment via Self-Play and Reflective Experience Replaycited by 6
  27. 2025DiffPO: Diffusion-styled Preference Optimization for Efficient Inference-Time Alignment of Large Language Modelscited by 5
  28. 2025AnyCap Project: A Unified Framework, Dataset, and Benchmark for Controllable Omni-modal Captioningcited by 5
  29. 2025Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Alignercited by 5
  30. 2025RPO: Fine-Tuning Visual Generative Models via Rich Vision-Language Preferencescited by 4
  31. 2025MUSE: MCTS-Driven Red Teaming Framework for Enhanced Multi-Turn Dialogue Safety in Large Language Modelscited by 4
  32. 2025Domain-Specific Constitutional AI: Enhancing Safety in LLM-Powered Mental Health Chatbotscited by 4
  33. 2025Benchmarking and Improving LLM Robustness for Personalized Generationcited by 4
  34. 2025SAFENLIDB: A Privacy-Preserving Safety Alignment Framework for LLM-based Natural Language Database Interfacescited by 4
  35. 2025InterMT: Multi-Turn Interleaved Preference Alignment with Human Feedbackcited by 3
  36. 2025Looking Beyond Visible Cues: Implicit Video Question Answering via Dual-Clue Reasoningcited by 3
  37. 2025daDPO: Distribution-Aware DPO for Distilling Conversational Abilitiescited by 3
  38. 2025SPaCe: Unlocking Sample-Efficient Large Language Models Training With Self-Pace Curriculum Learningcited by 3

50 most cited of 96 All 96 (JSON)

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  1. 2024Aligners: Decoupling LLMs and Alignment
  2. 2026AlignTune: Modular Toolkit for Post-Training Alignment of Large Language Models
  3. 2025Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges
  4. 2024Is Free Self-Alignment Possible?
  5. 2024MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time
  6. 2024A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques
  7. 2023Aligner: One Global Token is Worth Millions of Parameters When Aligning Large Language Models
  8. 2025SimPER: A Minimalist Approach to Preference Alignment without Hyperparameters