Skill-Based Mixture-of-Experts: Adaptive Routing for Heterogeneous Reasoning via Inferred Skills
arXiv:2503.05641v4 Announce Type: replace Abstract: Combining existing pre-trained LLMs is a promising approach for diverse reasoning tasks. However, task-level expert selection is often too coarse-grained, since different instances may require different expertise. To address this, we propose Skill-MoE, a symbolic, skill-based, and gradient-free Mixture-of-Experts framework for instance-level expert selection. Skill-MoE infers skills (e.g., algebra in mathematics) from each query, selects experts based on skill relevance, and lets each expert generate its own reasoning. The resulting k outputs
The increased scale and complexity of LLMs necessitate more efficient and adaptive reasoning mechanisms to handle diverse tasks effectively.
This development points towards more sophisticated and resource-efficient AI models, enabling a wider range of applications and reducing computational overhead.
AI models can now adapt their functionality at the instance level, becoming more versatile and potentially requiring less specialized retraining for varied tasks.
- · AI developers
- · Cloud computing providers
- · SaaS companies
- · Monolithic LLM approaches
More efficient and capable AI agents emerge for complex, heterogeneous reasoning.
This efficiency could lead to a faster deployment and wider adoption of AI in diverse professional settings.
The ability to dynamically combine and route expert knowledge might accelerate the development of truly generalized AI systems by abstracting skill-based reasoning.
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Read at arXiv cs.CL