Merging Text Transformer Models from Different Initializations (2403.00986v3)

Abstract: Recent work on permutation-based model merging has shown impressive low- or zero-barrier mode connectivity between models from completely different initializations. However, this line of work has not yet extended to the Transformer architecture, despite its dominant popularity in the language domain. Therefore, in this work, we investigate the extent to which separate Transformer minima learn similar features, and propose a model merging technique to investigate the relationship between these minima in the loss landscape. The specifics of the architecture, like its residual connections, multi-headed attention, and discrete, sequential input, require specific interventions in order to compute model permutations that remain within the same functional equivalence class. In merging these models with our method, we consistently find lower loss barriers between minima compared to model averaging, across models trained on a masked-LLMing task or fine-tuned on a language understanding benchmark. Our results show that the minima of these models are less sharp and isolated than previously understood, and provide a basis for future work on merging separately trained Transformer models.

• The paper introduces a one-shot permutation-based merging approach that effectively combines Transformer models from separate initializations.
• It demonstrates that Transformer minima exhibit shared features and reduced isolation in the loss landscape, challenging previous assumptions.
• The findings offer practical implications for optimization and ensembling strategies in deep learning, guiding more efficient training techniques.

Investigating the Connectivity of Transformative Models via One-Shot Permutation-Based Merging

Context and Background

Recent advancements have opened up a fascinating avenue in the field of neural network optimization and model merging. Notable among these is the exploration of low- or zero-barrier mode connectivity between models originating from distinct initializations. This property is exhibited when there are smoothly connected paths between models' minima in the loss landscape, maintaining high performance throughout the transition. While this phenomenon has been observed in various architectures, its examination within Transformer models, pivotal in the language processing domain, has been scant until now.

Research Insights

This paper makes several crucial contributions to the existing corpus of knowledge surrounding model merging and the underlying geometry of neural network loss landscapes. The authors propose a novel one-shot permutation-based model merging technique specifically tailored to Transformers. The technique underlines the importance of detailed interventions for accommodating the architectural nuances of Transformers, including their residual connections, multi-headed attention mechanisms, and discrete sequential inputs.

Key findings include:

1. A Novel Merging Algorithm: The introduction of a model merging algorithm based on model permutations is designed to combine Transformer models from separate initializations effectively. This method demonstrates reduced loss barriers between masked LLMs and fine-tuned models, indicating less isolated minima than previously thought.
2. Examination of Transformer Minima Similarities: The research explores how separate Transformer minima learn similar features, extending our understanding of loss landscape geometry to this architecture. It's highlighted that the minima of these models are less sharp and isolated than previously perceived.
3. Practical Implications: The findings suggest practical applications in optimization techniques, ensembling, and model merging strategies. For instance, a better understanding of loss geometry could inform the development of more effective training strategies for deep learning models.

Theoretical and Practical Implications

From a theoretical standpoint, this research sheds light on the symmetries and connectivity between different minima in the loss landscape of Transformer models. The demonstration of reduced loss barriers and the extension of these findings to fine-tuned models on benchmarks have significant implications:

• Optimization Techniques: Insights into the smoother loss landscape can guide the formulation of new optimization strategies that exploit the revealed connectivity for more efficient training.
• Ensembling Strategies: Understanding the connectivity between model minima can lead to more effective ensembling strategies that leverage the strengths of multiple models, potentially enhancing performance on various tasks.
• Future Merging Techniques: This work lays the groundwork for future investigations into merging techniques for separately trained Transformer models, possibly leading to novel approaches that conserve computational resources while maximizing model performance.

Future Directions

Looking ahead, the authors speculate on further investigation into the intricacies of connecting fine-tuned models, better characterizing the geometric properties of their minima, and exploring the significant variance in model connectivity across different tasks and datasets. Additionally, identifying the optimal types and quantities of data necessary for computing the most informative feature correlations stands as an open question for refining the proposed merging methodology.

Concluding Thoughts

In conclusion, this paper represents a pivotal step towards understanding the complex geometry of Transformer models' loss landscapes. The introduced one-shot permutation-based merging technique not only highlights the nuanced connectivity between separately initialized models but also prompts a reevaluation of prevailing assumptions about model performance and optimization strategies. As our grasp of these models' underlying landscapes evolves, so too will our capacity to innovate and enhance the foundational technologies driving advances in language processing and beyond.