Optimizing Model Merging to Mitigate Performance Tradeoffs

Developing large language models (LLMs) is a complex process that often leads to various checkpoints with different strengths and weaknesses. These models frequently exhibit performance differences across various tasks, such as following instructions or generating code. Instead of discarding these suboptimal checkpoints, as is common practice, new research explores the possibility of recycling these models through merging to create a Pareto-optimal model.

Model Merging as a Recycling Strategy

Traditionally, model merging is used to combine specialized expert models. The benefit of this method for merging generalist models trained on many tasks has been unclear. This new study examines merging in the context of large models (approximately 100 billion parameters) and focuses on recycling checkpoints that exhibit tradeoffs between different tasks. These checkpoints frequently arise during the development of a frontier model, but are often considered suboptimal and discarded.

The research investigates whether merging can transform such suboptimal models into a Pareto-optimal model. Starting from a pool of model checkpoints from different training runs (e.g., different phases, objectives, hyperparameters, and data mixtures), which naturally exhibit tradeoffs between different language capabilities, the study examines whether merging can transform these models into a Pareto-optimal model.

Optimization Algorithm for Linear Combination

The optimization algorithm used in the study adjusts the weight of each checkpoint in a linear combination. The result is a Pareto-optimal model that outperforms both individual models and merge-referenced baselines. Further analysis shows that good merges tend to incorporate almost all checkpoints with non-zero weights. This suggests that even seemingly poor initial checkpoints can contribute to good final merges.

Benefits of Optimized Model Merging

The research findings suggest that optimized model merging offers several advantages:

Cost-effectiveness: Instead of training new models from scratch, existing checkpoints can be reused, saving computation time and resources.

Performance improvement: Merging can lead to models that perform better across various tasks than the individual checkpoints.

Mitigation of performance tradeoffs: By optimizing the weights of the checkpoints, tradeoffs between different tasks can be minimized.

Outlook and Significance for AI Development

These research findings open up new possibilities for the development and optimization of LLMs. The ability to recycle suboptimal checkpoints could significantly increase the efficiency of model development and lead to more powerful and versatile language models. Especially for companies like Mindverse, which develop customized AI solutions, this approach offers the potential to optimize the development of chatbots, voicebots, AI search engines, and knowledge systems.

Optimized model merging represents a promising method for addressing the challenges of multitasking optimization in LLMs and exploiting the full potential of existing models. The research findings underscore the importance of recycling strategies in AI development and provide a foundation for future research in this area.

Bibliography Khalifa, M., et al. (2024). If You Can't Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs. arXiv preprint arXiv:2412.04144. https://arxiv.org/abs/2412.04144 https://arxiv.org/html/2412.04144v1 https://chatpaper.com/chatpaper/paper/87826 https://www.iflowai.com/static/chat/If%20You%20Can%27t%20Use%20Them%2C%20Recycle%20Them%3A%20Optimizing%20Merging%20at%20Scale%20Mitigates%20Performance%20Tradeoffs https://www.zhuanzhi.ai/paper/bb850f974f6d5b8174cba3da4ecbae71 https://www.researchgate.net/figure/Merges-found-via-CMA-ES-when-optimizing-MBPP-MUSR-tradeoffs-over-2-4-8-and-16_fig1_386502880 https://cohere.com/research https://www.catalyzex.com/author/Matthias%20Gall%C3%A9 https://www.giz.de/de/downloads/2021-06%20Design%20for%20recycling_barrierefrei.pdf https://cdrdv2-public.intel.com/821613/355308-Software-Optimization-Manual-048-Changes-Doc.pdf