SelFusion: Self-distillation for Diffusion Language Models
Diffusion language models (DLMs) alleviate the inherent latency bottleneck of autoregressive (AR) large language models (LLMs), but their degraded generation quality limits practical applicability. Although knowledge distillation (KD) can be a promising direction for improving performance, we empirically find that naively applying conventional KD yields only marginal gains, or even degrades generation quality. Based on these observations, we propose a novel self-distillation framework for DLMs, namely SelFusion. To enable effective KD without an external teacher model, SelFusion performs two forward passes with different masking levels, defining the hard mode with a larger masking probabi...
| Authors | Hyeongsoo Lim, Jinyoung Kim, Eunseo Seo et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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