CORA-Diff: Confidence-Oriented Residual Acceptance for Efficient Diffusion Language Model Inference: Diffusion language models...
(DLMs) update many tokens in parallel, yet practical decoders often use a fixed denoising horizon. Many predictions stabilize early, but blockwise decoding continues until all positions are resolved, causing repeated dense forward passes. Existing accelerators often rely on learned filters, modified scores, dependency models, or...
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CORA-Diff: Confidence-Oriented Residual Acceptance for Efficient Diffusion Language Model Inference: Diffusion language models (DLMs) update many tokens in parallel, yet practical decoders often use a fixed denoising horizon. Many predictions stabilize early, but blockwise decoding continues until all positions are resolved, causing repeated dense forward passes. Existing accelerators often rely on learned filters, modified scores, dependency models, or...