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

arXiv:2602.00217v2 Announce Type: replace Abstract: 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 extit{extbf{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 exttt{GPT2} and exttt{Qwen3-0.6B} exhibit severe condensation, whereas larger models such as exttt{GPT2-xl} and exttt{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.

Source: arXiv cs.LG | 2026-05-05