MoEvolve: A distillation method to strengthen MoE models
In brief
- MoEvolve uses MoE architecture to strengthen models without increasing inference cost.
- The method improves performance by up to 22.2 points on diverse benchmarks.
- Final models sometimes outperform their teachers after distillation.
A new distillation method, MoEvolve, enhances Mixture-of-Experts (MoE) language models by leveraging their internal architecture. It delivers significant gains on benchmarks while keeping inference activation budgets low.
This summary gives you the essentials. The full article, its figures and quotes are on cs.AI updates on arXiv.org.
Read the original articleAI-generated summary of the source, checked by rules. Report an error