MRP predicts logit residuals from hidden states to support dependency-aware multi-token denoising in a single forward pass for diffusion language models, yielding up to 1.42× lossless speedup on SDAR models.
Fast-decoding diffusion language models via progress-aware confidence schedules
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TAD improves the accuracy-parallelism trade-off in diffusion LLMs via temporal-aware self-distillation that applies hard labels to soon-to-be-decoded tokens and soft supervision to future tokens.
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Multi-Token Residual Prediction
MRP predicts logit residuals from hidden states to support dependency-aware multi-token denoising in a single forward pass for diffusion language models, yielding up to 1.42× lossless speedup on SDAR models.
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TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM
TAD improves the accuracy-parallelism trade-off in diffusion LLMs via temporal-aware self-distillation that applies hard labels to soon-to-be-decoded tokens and soft supervision to future tokens.