arXiv:2510.16138v2 Announce Type: replace Abstract: Existing expert merging strategies for Sparse Mixture of Experts (SMoE) typically rely on input-dependent or input-independent averaging of expert parameters, but often lack a principled weighting mechanism. In this work, we reinterpret expert merging through the lens of game theory, revealing cooperative and competitive dynamics among experts. Based on this perspective, we introduce Nash Merging of Experts (NAMEx), a novel framework that incorporates Nash Bargaining into the merging process, enabling more balanced and efficient collaboration
The continuous drive for more efficient and robust large language models (LLMs) and AI systems is leading to innovations in their fundamental architectural components, such as Sparse Mixture of Experts.
This development proposes a more principled and potentially more effective method for combining expert knowledge within AI models, addressing a critical challenge in scaling model performance and efficiency.
Current expert merging strategies often rely on simpler averaging, but this introduces game theory, suggesting a new paradigm for how AI 'experts' can collaborate or compete within an architecture, potentially leading to more balanced and efficient AI systems.
- · AI researchers
- · Large language model developers
- · Cloud providers leveraging efficient models
- · Companies deploying advanced AI
Improved performance and efficiency in complex AI models like Mixture of Experts architectures.
Reduced computational costs for training and inference of very large models, making them more accessible.
Acceleration of research into multi-agent AI systems, seeing individual AI components as 'players' in a strategic game.
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Read at arXiv cs.LG