mlsanityData to 14 Oct 2026

Hidden Dynamics of Massive Activations in Transformer Training

arXiv 2508.03616 ↗First posted 5 Aug 2025cs.AIcited by 10

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Abstract (arXiv)

We present the first comprehensive analysis of massive activation development throughout transformer training, using the Pythia model family as our testbed, and release our full dataset publicly to support further research. Through systematic analysis of various model sizes across multiple training checkpoints, we demonstrate that massive activation emergence follows highly predictable mathematical patterns that can be accurately modeled using an exponentially-modulated logarithmic function with five key parameters. Additionally, We develop a machine learning framework to predict these mathematical parameters from architectural specifications alone, achieving high accuracy for steady-state behavior and moderate accuracy for emergence timing and magnitude. These findings enable architects to predict and potentially control key aspects of massive activation emergence through design choices, with significant implications for model stability, training cycle length, interpretability, and optimization. Our findings demonstrate that the emergence of massive activations is governed by model design and can be anticipated, and potentially controlled, before training begins. Code is available at https://github.com/Aimpoint-Digital/massive-activations-fork

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 29 of its 45 references that are in the map. First posted on arXiv 5 Aug 2025.

Builds on

29 of 45 references.

References · 29

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  3. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  4. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  5. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  6. 2020Rethinking Attention with Performerscited by 1,601
  7. 2019Root Mean Square Layer Normalizationcited by 1,480
  8. 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,103
  9. 2023Vision Transformers Need Registerscited by 871
  10. 2022Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasetscited by 629
  11. 2024Massive Activations in Large Language Modelscited by 294
  12. 2022Towards Understanding Grokking: An Effective Theory of Representation Learningcited by 230
17 more
  1. 2024DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMscited by 167
  2. 2023Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothingcited by 151
  3. 2021Dive into Deep Learningcited by 111
  4. 2025Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understandingcited by 47
  5. 2025Peri-LN: Revisiting Normalization Layer in the Transformer Architecturecited by 34
  6. 2025Systematic Outliers in Large Language Modelscited by 33
  7. 2024Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation Regularizationcited by 23
  8. 2025Unleashing Diffusion Transformers for Visual Correspondence by Modulating Massive Activationscited by 23
  9. 2025Softpick: No Attention Sink, No Massive Activations with Rectified Softmaxcited by 22
  10. 2024Mitigating Quantization Errors Due to Activation Spikes in GLU-Based LLMscited by 21
  11. 2023Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversioncited by 16
  12. 2024Tracking the Feature Dynamics in LLM Training: A Mechanistic Studycited by 16
  13. 2024From Attention to Activation: Unravelling the Enigmas of Large Language Modelscited by 13
  14. 2025Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Modelscited by 13
  15. 2025A Refined Analysis of Massive Activations in LLMscited by 12
  16. 2024House of Cards: Massive Weights in LLMscited by 9
  17. 2024First Activations Matter: Training-Free Methods for Dynamic Activation in Large Language Modelscited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 9.

10 papers.

  1. 2025TWEO: Transformers Without Extreme Outliers Enables FP8 Training And Quantization For Dummiescited by 6
  2. 2026How Do Transformers Learn to Associate Tokens: Gradient Leading Terms Bring Mechanistic Interpretabilitycited by 3
  3. 2026Attention Sinks Induce Gradient Sinks: Massive Activations as Gradient Regulators in Transformerscited by 3
  4. 2026A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Modelscited by 2
  5. 2026Massive Activations in Hybrid Linear Attention Large Language Models: Pre-Attention Spikes and Inter-Spike Plateauscited by 2
  6. 2026A Unifying View of Attention Sinks: From Mechanisms to Architectural Interventionscited by 0
  7. 2026Transformer MLP Gate Thresholds Are Couplings to a Carried Reference Directioncited by 0
  8. 2026Which the Eye Fears: Writing with Read-Blindness Explains Massive Activations in Transformerscited by 0
  9. 2026The Life Cycle of a Massive Activation: Stochastic Birth, Weight-Decay-Driven Growth, and Competitive Consolidationcited by 0
  10. 2026Fault-tolerant foundation modelscited by 0