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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 conti
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