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Language Model Behavioral Phases are Consistent Across Architecture, Training Data, and Scale

large-language-models › training-methods

📄 Abstract

Abstract: We show that across architecture (Transformer vs. Mamba vs. RWKV), training dataset (OpenWebText vs. The Pile), and scale (14 million parameters to 12 billion parameters), autoregressive language models exhibit highly consistent patterns of change in their behavior over the course of pretraining. Based on our analysis of over 1,400 language model checkpoints on over 110,000 tokens of English, we find that up to 98% of the variance in language model behavior at the word level can be explained by three simple heuristics: the unigram probability (frequency) of a given word, the $n$-gram probability of the word, and the semantic similarity between the word and its context. Furthermore, we see consistent behavioral phases in all language models, with their predicted probabilities for words overfitting to those words' $n$-gram probabilities for increasing $n$ over the course of training. Taken together, these results suggest that learning in neural language models may follow a similar trajectory irrespective of model details.

Authors (3)

James A. Michaelov

Roger P. Levy

Benjamin K. Bergen

Submitted

October 28, 2025

arXiv Category

cs.CL

arXiv PDF

Key Contributions

This study reveals that autoregressive language models exhibit highly consistent behavioral phases during pretraining, irrespective of architecture (Transformer, Mamba, RWKV), training data, or scale. Up to 98% of word-level behavior variance can be explained by unigram probability, n-gram probability, and semantic similarity, suggesting fundamental, scale-invariant learning principles.

Business Value

Provides fundamental insights into how LLMs learn, enabling more efficient training strategies and better model design. Understanding these consistent patterns can lead to more predictable and reliable AI development.

Paper Metadata

Innovation Type

Analytical Finding/Methodology

Deployment Feasibility

High, as it's an analytical finding applicable to existing models and training processes.

Limitations Addressed

Lack of understanding regarding the consistency and underlying principles of LLM learning behavior across different model designs, datasets, and scales.

Performance Gains

Identified consistent behavioral phases across diverse LLMs.,Quantified the predictive power of simple heuristics (unigram, n-gram, semantic similarity) on model behavior (up to 98%).

Technical Tags

Language model behaviorPretraining dynamicsTransformerMambaRWKVBehavioral phasesUnigram probabilityN-gram probabilitySemantic similarityScale invarianceArchitecture invariance

Research Topics

Language Model TrainingLearning DynamicsModel Behavior AnalysisScaling LawsArchitectural Comparisons

Methods & Architectures

Analysis of over 1400 checkpointsWord-level behavior analysisCorrelation analysis TransformerMambaRWKV

Applications & Tasks

Fundamental AI research LLM development Model interpretability Understanding LLM learning processesIdentifying factors influencing model behaviorConsistency across architectures/data/scale Characterizing LLM behavior during pretrainingIdentifying universal learning principlesComparing different model architectures

Datasets & Benchmarks

Datasets

OpenWebText, The Pile

Word-level behaviorVariance explainedCorrelation coefficients

Related Fields

Natural Language ProcessingMachine LearningDeep LearningAI ResearchComputational Linguistics

Keywords

Language ModelsLLMPretrainingBehaviorDynamicsTransformerMambaRWKVScaleArchitectureUnigram probabilityN-gram probabilitySemantic similarityLearning phasesInvariance

Academic Context

#Language Model Training#Learning Dynamics#Model Behavior Analysis#Scaling Laws#Architectural Comparisons

Commercial Potential

Potential Products

More efficient LLM training methodologiesPredictive models for LLM behavior

Target Industries

TechnologyResearch and Development

Use Case Examples

Guiding the development of new LLM architectures by understanding which factors are most critical for learning.Optimizing training schedules based on observed behavioral phases.

Competitive Edge

Establishes a surprising degree of universality in LLM learning behavior, simplifying the understanding of complex models and suggesting common underlying principles across diverse architectures and scales.

Market Opportunity

Broad implications for the entire field of LLM development.

Revenue Models

N/A (fundamental research)

Resource Requirements

Compute Needs

High (analysis of 1400+ checkpoints)

Data Requirements

Large-scale pretraining datasets (OpenWebText, The Pile)

Deployment Constraints

Requires access to numerous model checkpoints for analysis.

Scalability

Findings suggest principles that hold across different scales.

Production Readiness

Maturity Level

Fundamental Research

Time to Market

N/A (fundamental research)

Patent Potential

Low (analytical findings)

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