How to Merge Your Multimodal Models Over Time? (2412.06712v1)

Abstract: Model merging combines multiple expert models - finetuned from a base foundation model on diverse tasks and domains - into a single, more capable model. However, most existing model merging approaches assume that all experts are available simultaneously. In reality, new tasks and domains emerge progressively over time, requiring strategies to integrate the knowledge of expert models as they become available: a process we call temporal model merging. The temporal dimension introduces unique challenges not addressed in prior work, raising new questions such as: when training for a new task, should the expert model start from the merged past experts or from the original base model? Should we merge all models at each time step? Which merging techniques are best suited for temporal merging? Should different strategies be used to initialize the training and deploy the model? To answer these questions, we propose a unified framework called TIME - Temporal Integration of Model Expertise - which defines temporal model merging across three axes: (1) Initialization Phase, (2) Deployment Phase, and (3) Merging Technique. Using TIME, we study temporal model merging across model sizes, compute budgets, and learning horizons on the FoMo-in-Flux benchmark. Our comprehensive suite of experiments across TIME allows us to uncover key insights for temporal model merging, offering a better understanding of current challenges and best practices for effective temporal model merging.

• The paper introduces the TIME framework that progressively merges multimodal models by optimizing both initialization and deployment strategies.
• It reveals that simpler methods like weighted averaging often perform comparably to complex merging techniques in dynamic, temporal settings.
• Experiments on 63 datasets validate that time-aware merging enhances scalability and continual learning in evolving AI systems.

Temporal Model Merging: An Insightful Exploration

The paper, "How to Merge Your Multimodal Models Over Time?" addresses a sophisticated problem space in AI: temporal model merging. Traditional model merging, often performed offline, involves integrating finetuned models derived from a shared base into a single model. However, in real-world scenarios, new tasks and data continually emerge. This creates the need for strategies to merge models progressively—a challenge the authors tackle with a proposed framework named TIME (Temporal Integration of Model Expertise).

Core Contributions and Framework

The primary contribution of the paper is introducing the TIME framework, which systematically addresses temporal model merging across three axes:

1. Initialization Phase: This phase concerns the strategy of choosing starting weights for expert models. The options include using the latest available weights, an exponential moving average of prior models, or starting fresh from the base model at each iteration.
2. Deployment Phase: Post-training, the challenge is selecting the final model to be deployed for downstream tasks. This decision must account for the cumulative knowledge of past models while integrating new task-specific insights.
3. Merging Techniques: The paper evaluates the efficacy of various merging strategies, from simple weight averaging to more complex schemes such as SLERP and TIES. A notable finding is that while complex techniques provide marginal gains, they are often outweighed by the importance of the initialization and deployment strategies.

Experimental Insights

Leveraging the multimodal FoMo-in-Flux benchmark with 63 datasets, the authors provide a comprehensive empirical paper. They find that standard offline merging techniques are inadequate in a temporal context. Key insights from the experiments include:

• Time Sensitivity: Strategies must account for the temporal dimension; offline methods struggle without time-aware adaptations.
• Complexity vs. Simplicity: Advanced merging techniques offer minimal benefits over simpler methods like weighted averaging.
• Initialization and Deployment Primacy: Effective performance hinges more on initialization and deployment strategies than on the sophistication of the merging technique.
• Scalability: Temporal model merging scales well with model size and compute resources, often surpassing multitask approaches, indicating its robustness and practicality.

Implications and Future Directions

This work effectively pushes the boundary of continual learning into the field of multimodal models, a critical progression as models move beyond text to incorporate increasingly diverse data sources. The findings imply a need for further exploration into refining initialization and deployment strategies to handle a broader range of models and tasks. Additionally, the scalability insights suggest that future research could delve into the implications of model size and complexity on temporal merging efficacy.

Conclusion

The research presented in this paper provides foundational insights into temporal model merging, offering a detailed framework and practical guidance for deploying models in dynamic, evolving environments. By introducing TIME, the authors fill an essential gap in the literature, highlighting the need for time-informed strategies in an ever-changing landscape of AI applications. This work invites further research to build upon these findings, extending temporal model merging methods to accommodate more complex, real-world scenarios.

In conclusion, the paper offers a substantial contribution to the field, setting the stage for ongoing advancements in adaptive, scalable AI systems capable of seamless integration of multimodal expertise over time.