SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization
Pretraining large language models (LLMs) with next-token prediction has led to remarkable advances, yet the context-dependent nature of token embeddings in such models results in high intra-class variance and inter-class similarity, thus hindering the efficiency of representation learning. While similarity-based regularization has demonstrated benefit in supervised fine-tuning and classification tasks, its application and efficacy in large-scale LLM pretraining remains underexplored. In this work, we propose the SimReg, an embedding similarity regularization loss that explicitly encourages token representations with the same ground-truth label within each sequence to be more similar, whil...
| Authors | Yan Sun, Guoxia Wang, Jinle Zeng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 May 2026 |
| Last updated at source | 9 May 2026 |
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