Author

Metod Jazbec

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#machine learning Preprint Sep 2026

Fork-dLLM: Avoiding the Flexibility Trap in Diffusion Language Models

Masked diffusion language models (dLLMs) have shown strong potential for faster inference through parallel token generation when combined with confidence-based samplers. However, recent work has shown that such methods can defer unmasking high-entropy fork positions at which multiple plausible continuations exist. This...

S. Frkovic, Metod Jazbec, C. Naesseth · 0 citations

#machine learning Preprint Sep 2026

Temporal Self-Distillation: Faster Inference in Discrete Diffusion Language Models

Temporal Self-Distillation (TSD), a simple on-policy method that trains dLLMs for fast inference by distilling predictions across time, substantially shifts the speed--quality frontier toward the low-compute regime.

Shi-Jia Xu, Andrea Miele, Metod Jazbec et al. · 0 citations