mlsanityData to 7 Oct 2026

Understanding Layer Significance in LLM Alignment

arXiv 2410.17875 ↗First posted 23 Oct 2024cs.CL cs.AIcited by 19

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

Abstract (arXiv)

Aligning large language models (LLMs) through supervised fine-tuning is essential for tailoring them to specific applications. Recent studies suggest that alignment primarily adjusts a model's presentation style rather than its foundational knowledge, indicating that only certain components of the model are significantly impacted. To uncover how alignment affects model behavior at a granular level, we propose identifying which layers within LLMs are most critical to the alignment process. Our approach, named ILA, involves learning a binary mask for the parameter changes in each layer during alignment, as an indicator of layer significance. Experimental results reveal that, despite substantial differences in alignment datasets, the important layers of a model identified by ILA exhibit nearly 90% overlap, highlighting fundamental patterns in LLM alignment. The results also indicate that freezing non-essential layers improves overall model performance, while selectively tuning the most critical layers significantly enhances fine-tuning efficiency with minimal performance loss. Finally, we discuss how these findings extend from LLM alignment to reasoning.

Key facts. Cited by 19 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 69 of its 84 references that are in the map. First posted on arXiv 23 Oct 2024.

Builds on

69 of 84 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Spectral Editing of Activations for Large Language Model Alignment15 May
  2. 2024The Real, the Better: Aligning Large Language Models with Online Human Behaviors1 May
  3. 2023The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning4 Dec
  4. 2023Preference Ranking Optimization for Human Alignment30 Jun
  5. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructions20 Dec

Other references · 64

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  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. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  7. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  8. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  9. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,018
  10. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  11. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  12. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
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  4. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  5. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  6. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,928
  7. 2021BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelscited by 1,234
  8. 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 876
  9. 2022Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Taskscited by 858
  10. 2023StarCoder: may the source be with you!cited by 790
  11. 2023WizardCoder: Empowering Code Large Language Models with Evol-Instructcited by 728
  12. 2023MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Modelscited by 724
  13. 2023WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instructcited by 618
  14. 2023Instruction Tuning for Large Language Models: A Surveycited by 617
  15. 2023Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruningcited by 408
  16. 2020Parameter-Efficient Transfer Learning with Diff Pruningcited by 351
  17. 2023AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuningcited by 337
  18. 2024PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Modelscited by 261
  19. 2021ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generationcited by 258
  20. 2023The False Promise of Imitating Proprietary LLMscited by 254
  21. 2024The Unreasonable Ineffectiveness of the Deeper Layerscited by 219
  22. 2023OctoPack: Instruction Tuning Code Large Language Modelscited by 178
  23. 2022Structured Pruning Learns Compact and Accurate Modelscited by 167
  24. 2023Platypus: Quick, Cheap, and Powerful Refinement of LLMscited by 150
  25. 2021Composable Sparse Fine-Tuning for Cross-Lingual Transfercited by 144
  26. 2024From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Modelscited by 136
  27. 2021Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minimacited by 118
  28. 2023Task-Specific Skill Localization in Fine-tuned Language Modelscited by 104
  29. 2020Self-training Improves Pre-training for Natural Language Understandingcited by 96
  30. 2020Masking as an Efficient Alternative to Finetuning for Pretrained Language Modelscited by 92
  31. 2023Neurons in Large Language Models: Dead, N-gram, Positionalcited by 88
  32. 2024LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuningcited by 88
  33. 2022ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Promptscited by 85
  34. 2023Contrastive Preference Learning: Learning from Human Feedback without RLcited by 77
  35. 2022Revisiting Parameter-Efficient Tuning: Are We Really There Yet?cited by 76
  36. 2023Recon: Reducing Conflicting Gradients from the Root for Multi-Task Learningcited by 50
  37. 2023Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Modelscited by 42
  38. 2024Investigating Layer Importance in Large Language Modelscited by 35

50 most cited of 64 All 64 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 11; 2026: 8.

19 papers.

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  1. 2025A Survey on Latent Reasoningcited by 71
  2. 2025Encode, Think, Decode: Scaling test-time reasoning with recursive latent thoughtscited by 23
  3. 2025Recurrent Knowledge Identification and Fusion for Language Model Continual Learningcited by 14
  4. 2026FOREVER: Forgetting Curve-Inspired Memory Replay for Language Model Continual Learningcited by 7
  5. 2025GeoEdit: Geometric Knowledge Editing for Large Language Modelscited by 6
  6. 2025AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learningcited by 4
  7. 2025Alignment-Constrained Dynamic Pruning for LLMs: Identifying and Preserving Alignment-Critical Circuitscited by 4
  8. 2026PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMscited by 3
  9. 2026Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Trainingcited by 3
  10. 2025ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Modelscited by 2
  11. 2026Swift-SVD: Theoretical Optimality Meets Practical Efficiency in Low-Rank LLM Compressioncited by 2
  12. 2025An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruningcited by 1
7 more
  1. 2025LoRA-PAR: A Flexible Dual-System LoRA Partitioning Approach to Efficient LLM Fine-Tuningcited by 1
  2. 2025MACE: A Hybrid LLM Serving System with Colocated SLO-aware Continuous Retraining Alignmentcited by 1
  3. 2025Feature-Guided SAE Steering for Refusal-Rate Control using Contrasting Promptscited by 1
  4. 2026When Gradient Importance Lies: Adaptive LoRA Rank Allocation Fails Under GRPOcited by 0
  5. 2026Rotation-Preserving Supervised Fine-Tuningcited by 0
  6. 2026From Parameters to Data: A Task-Parameter-Guided Fine-Tuning Pipeline for Efficient LLM Alignmentcited by 0
  7. 2026Micro-Macro Retrieval: Reducing Long-Form Hallucination in Large Language Modelscited by 0

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  3. 2025Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality
  4. 2026Alignment Dynamics in LLM Fine-Tuning
  5. 2025Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges
  6. 2024Efficient Alignment of Large Language Models via Data Sampling
  7. 2024Aligners: Decoupling LLMs and Alignment
  8. 2023Large Language Model Alignment: A Survey