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CoDR: Training-Free Confidence-Drift Remasking for Diffusion Language Models
Yue Wu, Author 2, Author 3, Author 4, Author 5
masked diffusionlanguage modelsremaskingconfidence drift
2610.08833
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1h ago80%
Abstract
This paper introduces CoDR, a method that addresses confidence drift in masked diffusion language models by remasking and regenerating tokens based on their confidence levels.
Reality Card
Core Claim
CoDR improves average accuracy across various model-sampler configurations while using far fewer forward passes than prior remasking methods.
Method / Result
CoDR improves accuracy across all evaluated model-sampler configurations with modest overhead.
Limitations
The method's effectiveness may vary based on the specific model and sampler configurations used.
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