arXiv Notify me• Aug 26, 2024paper

A Practitioner's Guide to Continual Multimodal Pretraining

Oriol Vinyals(guest)

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Executive Summary

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The Challenge of Model Obsolescence Bridging the Gap in Continual Learning Research

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Concerns Raised

Foundation models rapidly become outdated in real-world applications.
Existing continual learning research often focuses on impractical, extreme scenarios.
The complexity of choosing the right update strategy (e.g., fine-tuning, PEFT, model merging) for a given scenario.

Opportunities Identified

Developing more efficient and effective methods for keeping large models current.
Adapting foundation models to new domains and tasks without catastrophic forgetting.
Leveraging the FoMo-in-Flux benchmark to standardize and accelerate research in continual multimodal pretraining.

Key Themes

Research Findings3

The FoMo-in-Flux benchmark for continual multimodal pretraining was constructed using 63 datasets and is designed with realistic compute constraints and practical deployment requirements.

Research using the FoMo-in-Flux benchmark investigated continual pretraining by analyzing data mixtures, stream orderings, various update methods including fine-tuning and model merging, meta learning rate schedules, and the effects of model and compute scaling.

The benchmark and code for FoMo-in-Flux are publicly available on GitHub in the ExplainableML/fomo_in_flux repository.

Topics

Continual Learning Multimodal Models Foundation Models Vision-Language Models (VLM)Pretraining Model Updates AI Benchmarking FoMo-in-Flux Parameter-Efficient Fine-Tuning (PEFT)Model Merging Catastrophic Forgetting Machine Learning Operations (MLOps)Data-centric AI Model Lifecycle Management Artificial Intelligence

Processed Jul 28, 2026Daily intelligence brief → yt-dlp + mlx-whisper + Gemini

A Practitioner's Guide to Continual Multimodal Pretraining

Continue your research

Concerns Raised

Opportunities Identified

Key Themes

The Challenge of Model Obsolescence

Bridging the Gap in Continual Learning Research

A Standardized Benchmark for Realistic Evaluation

A Practitioner's Guide to Update Methodologies

Research Findings3

Topics