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.
Fast-Decoding Diffusion Language Models via Progress-Aware Confidence Schedules
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Fast-dLLM++ generalizes Fast-dLLM decoding to heterogeneous confidence profiles via Fréchet profile selection, delivering up to 37% throughput gains on GSM8K, MATH, HumanEval, and MBPP with LLaDA-8B.
MRP predicts logit residuals between adjacent denoising steps in DLMs from backbone hidden states to support efficient multi-token denoising, yielding up to 1.4x lossless speedup or 22.6-point accuracy gains on code and math tasks.
citing papers explorer
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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.
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Fast-dLLM++: Fr\'{e}chet Profile Decoding for Faster Diffusion LLM Inference
Fast-dLLM++ generalizes Fast-dLLM decoding to heterogeneous confidence profiles via Fréchet profile selection, delivering up to 37% throughput gains on GSM8K, MATH, HumanEval, and MBPP with LLaDA-8B.
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Multi-Token Residual Prediction
MRP predicts logit residuals between adjacent denoising steps in DLMs from backbone hidden states to support efficient multi-token denoising, yielding up to 1.4x lossless speedup or 22.6-point accuracy gains on code and math tasks.