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.
Learning to parallel: Accelerating diffusion large language models via learnable parallel decoding
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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.
STDec raises dLLM decoding speed by up to 14x on benchmarks like MBPP by using observed spatio-temporal stability to create dynamic, token-specific confidence thresholds while preserving task performance.
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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STDec: Spatio-Temporal Stability Guided Decoding for dLLMs
STDec raises dLLM decoding speed by up to 14x on benchmarks like MBPP by using observed spatio-temporal stability to create dynamic, token-specific confidence thresholds while preserving task performance.