Dispersion Loss Counteracts Embedding Condensation and Improves Generalization in Small Language Models

Large language models (LLMs) achieve remarkable performance through ever-increasing parameter counts, but scaling incurs steep computational costs. To better understand LLM scaling, we study representational differences between LLMs and their smaller counterparts, with the goal of replicating the representational qualities of larger models in smaller models. We observe a geometric phenomenon which we term $\textbf{embedding condensation}$, where token embeddings collapse into a narrow cone-like subspace in some language models. Through systematic analyses across multiple Transformer families, we show that small models such as $\texttt{GPT2}$ and $\texttt{Qwen3-0.6B}$ exhibit severe condensation, whereas larger models such as $\texttt{GPT2-xl}$ and $\texttt{Qwen3-32B}$ are more resistant to this phenomenon. Additional observations show that embedding condensation is not reliably mitigated by knowledge distillation from larger models. To fight against it, we formulate a dispersion loss that explicitly encourages embedding dispersion during training. Experiments demonstrate that it mitigates condensation, recovers dispersion patterns seen in larger models, and yields performance gains across 10 benchmarks. We believe this work offers a principled path toward improving smaller Transformers without additional parameters.

Language models represent each word or token as a point in a high dimensional space. We found that in many language models, these points gradually collapse into a narrow cone as they pass through the model's layers, leaving much of the representation space unused. We call this phenomenon embedding condensation. This collapse is much stronger in smaller models than in larger models from the same family, and it appears consistently across different model families, datasets, and under confounder-controlled settings. Surprisingly, embedding condensation is already present at initialization, and is not reliably solved by distilling knowledge from a larger model. These findings suggest that smaller language models are not only limited by having fewer parameters, but also by how their internal representations are geometrically organized. To test whether this geometry can be improved, we introduce a simple training objective that encourages token representations to spread out more evenly. This reduces embedding condensation and modestly improves generalization, pointing to representation geometry as a useful direction for making language models more capable.