Optimizing VLM Pretraining Data Mixes

DecoupleMix offers a systematic, attributable approach to optimizing Vision Language Model pretraining data mixtures, achieving strong performance with greater efficiency.

Jul 28 at 8:01 PM6 min read

Abstract representation of data streams converging into a central AI model. — Visualizing the systematic approach to data mixture composition in Vision Language Model pretraining.

Visual TL;DR. Heuristic VLM Pretraining leads to Opaque Data Curation. Heuristic VLM Pretraining addresses DecoupleMix Framework. Opaque Data Curation solves DecoupleMix Framework. DecoupleMix Framework enables Decouple Data Mix. Decouple Data Mix achieves Systematic Optimization. Systematic Optimization results in Stronger VLM Performance. DecoupleMix Framework uses Inter-class Ratios. DecoupleMix Framework uses Intra-class Ratios. Inter-class Ratios contributes to Systematic Optimization. Intra-class Ratios contributes to Systematic Optimization.

Heuristic VLM Pretraining: stacking datasets based on ad-hoc quality filters and intuitive domain ratios
Opaque Data Curation: hinders reproducible advancements and leaves frontier recipes undisclosed to practitioners
DecoupleMix Framework: systematic, attributable approach to optimizing VLM pretraining data mixtures
Decouple Data Mix: transforms data construction into a systematic mixture-optimization problem for reproducibility
Inter-class Ratios: managed through a single-variable iterative search across different capabilities
Intra-class Ratios: multidimensional, dataset-level assessment within a specific category for composition
Systematic Optimization: decoupling complex mixture problem into two orthogonal sub-problems for efficiency
Stronger VLM Performance: achieving strong performance with greater efficiency in Vision Language Models

Visual TL;DRQuickExplainDeeper

The current landscape of Vision Language Model (VLM) pretraining is plagued by heuristic data curation. Practitioners often stack datasets based on ad-hoc quality filters and intuitive domain ratios, lacking a principled approach for new data inclusion. This opacity hinders reproducible advancements and leaves frontier recipes undisclosed. To address this, researchers have introduced DecoupleMix, a framework that transforms data construction into a systematic mixture-optimization problem, establishing it as a reproducible engineering discipline.

Decomposing the Data Mixture Challenge

DecoupleMix achieves this by decoupling the complex mixture problem into two orthogonal sub-problems: inter-class ratios across different capabilities and intra-class ratios within a specific category. Inter-class allocation is managed through a single-variable iterative search, while intra-class composition employs a multidimensional, dataset-level assessment. This assessment scores datasets on Quality and Difficulty, formulating selection as a constrained convex optimization problem with a diversity objective.

Enabling Attributable and Guided Data Curation

This systematic approach yields two critical capabilities. Firstly, it provides clear guidance on what data to collect next, moving beyond guesswork. Secondly, it renders dataset validation a controlled, attributable experiment. Experiments demonstrate that DecoupleMix consistently surpasses heuristic baselines. Notably, optimal ratios discovered on small-scale proxies transfer seamlessly to larger scales without requiring retuning. Furthermore, by utilizing 80B additional multimodal continue-pretraining tokens, a VLM trained with this method proved competitive against strong open-source models trained with substantially larger multimodal budgets, highlighting the efficiency gains achievable in Vision Language Model pretraining.

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#AI Research #VLM #Data Optimization #Machine Learning