Focus on the Core: Empowering Diffusion Large Language Models by Self-Contrast
The iterative denoising paradigm of Diffusion Large Language Models (DLMs) endows them with a distinct advantage in global context modeling. However, current decoding strategies fail to leverage this capability, typically exhibiting a local preference that overlooks the heterogeneous information density within the context, ultimately degrading generation quality. To address this limitation, we systematically investigate high-information-density (HD) tokens and present two key findings: (1) explicitly conditioning on HD tokens substantially improves output quality; and (2) HD tokens exhibit an early-decoding tendency, converging earlier than surrounding tokens. Motivated by these findings,...
| Authors | Jinyuan Feng, Xin Yu, Yiqun Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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