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AI Research
arXiv CS.AI·1d ago·5 min read
SLEM: Spectrum-Aware Latent Expert Merging
TL;DR
Spectrum-Aware Latent Expert Merging (SLEM) introduces a novel framework for merging experts in Mixture-of-Experts models, reducing memory overhead without calibration data. SLEM achieves competitive performance on knowledge benchmarks and excels in tasks like mathematical reasoning and code generation.
✦ Why It Matters
Implement SLEM to optimize memory usage in your MoE models while maintaining or improving performance.
Key Takeaways
- Explore SLEM for efficient expert merging in your models.
- Utilize latent representations to reduce memory overhead.
- Focus on geometry-aware interpolation for better performance.
- Test SLEM on generation tasks like code generation.
How It Works
SLEM employs a Variational Autoencoder (VAE) to create a structured latent representation of expert weights. It groups experts based on latent similarities and calculates merging coefficients from their weight spectra.
This allows for geometry-aware latent interpolation, which effectively merges expert functions while maintaining their performance characteristics.