Scaling Embeddings Outperforms MoE in LLMs

Description

In this AI Research Roundup episode, Alex discusses the paper: 'Scaling Embeddings Outperforms Scaling Experts in Language Models' This paper introduces N-gram Embedding layers as a more efficient way to scale language models compared to the popular Mixture-of-Experts approach. By using vocabulary-free tables indexed via polynomial rolling hashes, the researchers achieved constant time lookup complexity without the communication overhead typically found in expert scaling. Their experiments show that embedding scaling is most effective when introduced after expert scaling reaches its peak efficiency. To avoid performance drops, they suggest keeping the embedding parameter budget under 50 percent of the total model. This research provides a new architectural principle for building powerful LLMs with fewer system bottlenecks. Paper URL: https://arxiv.org/abs/2601.21204 #AI #MachineLearning #DeepLearning #LLM #ScalingLaws #MixtureOfExperts #NLP Resources: - Hugging Face model: https://huggingface.co/meituan-longcat/LongCat-Flash-Lite