Diffusion LLMs show greater representational redundancy, enabling compression

A new paper analyzes the internal representations of autoregressive (AR) and diffusion language models (dLLMs). Researchers found that diffusion models create more global representations with early-layer redundancy, unlike AR models which have tightly coupled, local representations. This redundancy in dLLMs allows for significant computational savings, with native diffusion models absorbing up to 18.75% FLOPs reduction while maintaining over 90% performance on math and coding tasks. AI

影响 Diffusion LLMs show potential for significant computational efficiency gains through inherent representation redundancy.

排序理由 Academic paper analyzing internal representations of different LLM training objectives.

在 arXiv cs.CL 阅读 →

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