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PACE-dLLM: Elastic Block Decoding via Confidence Cliff Estimation for Diffusion Language Models
Diffusion language models (dLLMs), such as LLaDA and Dream, have become competitive with autoregressive (AR) LLMs in generation quality while supporting native parallel decoding. A standard acceleration strategy is block-wise decoding, where each forward pass predicts a block of length B and commits
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