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arXiv Machine Learning By Mathias Ollu, Nikos Komodakis

Denoising Hierarchical Representations: Joint Continuous Diffusion for Language Modeling

Read the original on arXiv Machine Learning →

The paper introduces Hierarchical Continuous Diffusion Language Models (H-CDLMs), a framework that jointly diffuses multiple token modalities—individual tokens and coarser clusters of token embeddings—to enhance continuous diffusion language models. Applied to the CoBit architecture, the resulting H-CoBit achieves significant empirical gains, improving MAUVE scores and achieving lower generative perplexity on LM1B and OWT, while also outperforming prior continuous diffusion models on GSM8K. The approach generalizes to other continuous generative paradigms, as shown by consistent improvements when applied to the flow matching model FLM.

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