arxiv_ai 95% Match Research Paper AI Researchers,Machine Learning Engineers,LLM Developers 4 weeks ago

Training Optimal Large Diffusion Language Models

large-language-models › training-methods

📄 Abstract

Abstract: We introduce Quokka, the first systematic scaling law for diffusion language models (DLMs), encompassing both compute-constrained and data-constrained regimes, and studying the key modeling and optimization designs. Quokka is a good friend of Chinchilla and provides wider scopes. We hope the results would bring short-term practical guidance in DLMs training and long-term inspirations for the whole AI community.

Key Contributions

Introduces Quokka, the first systematic scaling law for diffusion language models (DLMs), covering both compute-constrained and data-constrained regimes. This work provides practical guidance for DLM training and long-term inspiration for the AI community by studying key modeling and optimization designs.

Business Value

Provides a framework for more efficient and effective training of large language models, potentially reducing computational costs and accelerating development cycles for AI products.

Paper Metadata

Innovation Type

Theoretical Framework

Deployment Feasibility

High, as it focuses on training methodologies rather than specific deployment challenges.

Limitations Addressed

Lack of systematic understanding of scaling laws for diffusion language models.

Technical Tags

Diffusion Language ModelsScaling LawsModel OptimizationCompute-Constrained TrainingData-Constrained TrainingLarge Language ModelsAI TrainingModel Design

Research Topics

Large Language ModelsModel ScalingTraining EfficiencyAI ResearchGenerative ModelsDiffusion Models

Methods & Architectures

Scaling Law AnalysisModel Optimization Techniques Diffusion Language Models (DLMs)

Applications & Tasks

Natural Language Processing AI Model Development Optimizing LLM TrainingUnderstanding Scaling Behavior Training Large Language ModelsDeveloping Diffusion Language Models

Related Fields

Machine LearningDeep LearningNatural Language ProcessingGenerative Models

Keywords

Diffusion Language ModelsScaling LawsLLM TrainingCompute EfficiencyData EfficiencyModel OptimizationAIMachine LearningDeep LearningNLPGenerative AIQuokka

Academic Context

#Large Language Models#Model Scaling#Training Efficiency#AI Research#Generative Models#Diffusion Models

Commercial Potential

Potential Products

More efficient LLM training platformsTools for optimizing DLM development

Target Industries

TechnologyAI Research and Development

Use Case Examples

Guiding the training of new large diffusion language modelsOptimizing resource allocation for LLM training

Competitive Edge

Offers a novel theoretical framework for understanding and optimizing DLM training, complementing existing empirical approaches.

Market Opportunity

Large (related to the growing LLM market)

Resource Requirements

Compute Needs

High (implied by 'large diffusion language models' and 'compute-constrained regimes')

Data Requirements

Large-scale datasets (implied by 'data-constrained regimes')

Scalability

Focuses on scaling laws, implying scalability is a core consideration.

Production Readiness

Maturity Level

Research/Theoretical

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