If You Can't Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs (2412.04144v3)

Abstract: Model merging has shown great promise at combining expert models, but the benefit of merging is unclear when merging "generalist" models trained on many tasks. We explore merging in the context of large (~100B) models, by recycling checkpoints that exhibit tradeoffs among different tasks. Such checkpoints are often created in the process of developing a frontier model, and the suboptimal ones are usually discarded. Given a pool of model checkpoints obtained from different training runs (e.g., different stages, objectives, hyperparameters, and data mixtures), which naturally show tradeoffs across different language capabilities (e.g., instruction following vs. code generation), we investigate whether merging can recycle such suboptimal models into a Pareto-optimal one. Our optimization algorithm tunes the weight of each checkpoint in a linear combination, resulting in such an optimal model that outperforms both individual models and merge-based baselines. Further analysis shows that good merges tend to include almost all checkpoints with non-zero weights, indicating that even seemingly bad initial checkpoints can contribute to good final merges.

• The paper introduces an evolutionary optimization framework that linearly combines discarded LLM checkpoints to achieve Pareto-optimal merges, reducing performance tradeoffs.
• It applies a scalable evolutionary algorithm that fine-tunes weights for over 100B-parameter models, balancing multi-task performance across various benchmarks.
• The study demonstrates that even suboptimal checkpoints contribute positively to model merging, highlighting a sustainable approach to LLM development.

Optimizing Merging at Scale: An Analytical Perspective

The paper, "If You Can't Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs," investigates the potential of model merging to minimize performance tradeoffs in LLMs, specifically in the field of substantial models around 100 billion parameters. The study explores the context of "recycling" checkpoints generated during LLM development, which often represent different stages, objectives, and data mixtures with inherent tradeoffs across various linguistic capabilities.

Core Methodology

The authors propose an optimization algorithm grounded in linear combination of model checkpoints to transform discarded suboptimal models into Pareto-optimal ones. By treating the checkpoints from different runs as a pool, the methodology explores whether an effective linear merge can result in a model outperforming its constituents. Using evolutionary optimization, they tune the weight assigned to each checkpoint, aiming to mitigate task tradeoffs without additional retraining phases.

Three primary contributions underscore this work:

1. Unexplored Setup Exploration: The paper explores merging to balance performance across a diverse set of multi-tasking, generalist LLM checkpoints. This differs markedly from previous literature focused on smaller, specialized "expert" model merging.
2. Evolutionary Optimization Application: The study utilizes evolutionary algorithms to effectively search and optimize checkpoint weightings, a more scalable and consistent alternative compared to manual tuning.
3. Comprehensive Analysis on Merging Dynamics: The analysis suggests that good merges often include most checkpoints with non-zero weights, positing that even underperforming models can enhance merge outcomes.

Results and Implications

The research reveals several noteworthy results in benchmark evaluations across tasks like code, math, and instruction following. The key takeaways are:

• Task Tradeoff Reduction: Search-optimized merging significantly reduces task tradeoffs over baselines like uniform and greedy merges, confirming the potential of large-scale model merging in LLM contexts.
• Model Recycling: Highlighting efficiency, the method illustrates that merging can leverage previously suboptimal checkpoints as valuable components for optimized models.
• Robustness and Generalization: The resulting merged models retained their competitiveness on out-of-domain tasks, indicating broad applicability beyond the specific tasks used for optimization.

Considerations and Future Directions

The implications of the paper suggest potential shifts in how model training and resource allocation are approached. It advocates for a paradigm where intermediate models are not merely seen as failed experiments but are instead leveraged as part of a cyclical and sustainable development process. This perspective not only reduces computational waste but also envisions a cost-effective avenue to achieve superior performance across diverse tasks.

Future research directions could focus on integrating more sophisticated strategies for checkpoint selection prior to optimization and exploring adaptive weighting schemes that react to checkpoint performance dynamically. Expanding this methodology to merge models trained under disparate architectures or even different languages could further enhance the versatility of model merging frameworks.

Conclusion

In essence, the study introduces a compelling case for recycling in the context of foundational models. By rigorously analyzing merging strategies, the paper not only provides a new lens to view model training pipelines but also suggests substantial efficiency and performance dividends within the ecosystem of LLM development. As AI continues to scale and integrate globally, such strategies will be pivotal in maintaining sustainable progress while effectively optimizing model capabilities.