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cloud.google.com·6h ago
AI Research
arXiv CS.AI·25d ago·5 min read
MoRE: Mixture of Reused Experts
TL;DR
MoRE introduces a novel Mixture of Reused Experts architecture that enhances language modeling by sharing expert pools across layers, achieving superior performance with fewer parameters. It consistently outperforms traditional Mixture-of-Experts models and weight-sharing architectures in perplexity and downstream tasks.
✦ Why It Matters
Consider implementing MoRE in your next language modeling project to enhance performance without increasing resource costs.
Key Takeaways
- Explore MoRE for improved language modeling efficiency.
- Utilize shared expert pools to reduce memory usage.
- Implement lightweight depth embeddings for better layer differentiation.
- Test MoRE against existing models to evaluate performance gains.
How It Works
MoRE operates by sharing a pool of experts across adjacent layers in a neural network, allowing each layer to select from a larger set of experts while retaining its own routing mechanism. This approach increases the diversity of routing combinations without the need for additional parameters.
The introduction of lightweight learnable depth embeddings enables the model to condition inputs based on the layer, ensuring that shared experts can effectively distinguish their roles in the architecture.