Preprint Open access
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 …