Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models
Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference steps within a single architecture, none address cross-architecture knowledge transfer, in which the teacher and student differ in architecture, attention mechanism, and tokenizer. We present TIDE, the first framework for cross-architecture dLLM distillation, comprising three modular components: (1) TIDAL, which jointly modulates distillation strength across training progress and diffusion timestep to account for the teacher's noise-dependent reliabili...
| Authors | Gongbo Zhang, Wen Wang, Ye Tian et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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